Optical imaging methods

ABSTRACT

Methods for optically imaging blood flow changes, blood flow characteristics and changes in the oxygenation of blood in an area of interest are disclosed. The area of interest is illuminated with electromagnetic radiation(emr) in the visible or infrared regions of the spectrum, and a control image representative of the emr absorption of the area of interest is acquired. A subsequent image representative of the emr absorption is compared to the control image to detect changes in the emr absorption that are indicative of changes in blood flow, changes in blood flow characteristics, or changes in blood oxygenation in the area of interest.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a continuation-in-part patent of application Ser. No.08/073,353, filed Jun. 7, 1993 now U.S. Pat. No. 5,465,718, which is acontinuation-in-part of application Ser. No. 07/894,270, filed on Jun.8, 1992 now U.S. Pat. No. 5,438,989, which is a continuation-in-part ofapplication Ser. No. 07/565,454, filed Aug. 10, 1990, now U.S. Pat. No.5,215,095.

TECHNICAL FIELD OF THE INVENTION

The present invention provides a method for real-time detection of solidtumor tissue, plus an ability to grade and characterize tumor tissue.The present invention further provides a method for real-time mapping offunctional and dysfunctional cerebral cortex and nervous tissue. Thepresent invention further provides a device for real-time detection andoptical imaging for the inventive methods.

BACKGROUND OF THE INVENTION

A primary goal of neurological surgery is the complete removal ofabnormal or pathological tissue while sparing normal areas. Hence, theneurosurgeon attempts to identify boundaries of pathological ordysfunctional tissue and to map adjacent areas of the cortex committedto important functions, such as language, motor and sensory areas sothat pathological/dysfunctional tissue is removed without removingfunctional areas.

Incidence rates for primary intracranial brain tumors are in the rangeof 50-150 cases per million population or about 18,000 cases per year(Berens et al. 1990). Approximately one half of brain tumors aremalignant. The incidence of malignant brain tumors in adults ispredominantly in the age range of 40-55 years while the incidence ofmore benign tumors peaks near 35 years of age. A primary means fortreatment of such tumors is surgical removal. Many studies have shownthat when more of the total amount of tumor tissue is removed, thebetter the clinical outcome. For gross total resections of tumors, the5-year survival rate is doubled when compared to subtotal resection.Both duration of survival and independent status of the patient areprolonged when the extent of resection is maximized in malignantgliomas. Current intraoperative techniques do not provide rapiddifferentiation of tumor tissue from normal brain tissue, especiallyonce the resection of the tumor begins. Development of techniques thatenhance the ability to identify tumor tissue intraoperatively may resultin maximizing the degree of tumor resection and prolonging survival.

Of the 500,000 patients projected to die of systemic cancer per year inthe United States, approximately 25%, or over 125,000 can be expected tohave intracranial metastasis. The primary focus for surgery in thisgroup is in those patients with single lesions who do not havewidespread or progressive cancer. This group represents about 20-25% ofpatients with metastases (30,000), however, the actual number ofpatients that are good candidates for surgery is slightly smaller. Ofthose patients undergoing surgery, one half will have local recurrenceof their tumor at the site of operation, while the other half willdevelop a tumor elsewhere. The fact that about 50% of the surgeries failat the site of operation means that an improved ability to remove asmuch tumor as possible by detecting and localizing tumor margins duringtumor removal could potentially decrease the incidence of localrecurrence.

Thus, for both primary and metastatic tumors, the more tumor tissueremoved, the better the outcome and the longer the survival. Further, bymaximizing the extent of resection, the length of functional, goodquality survival is also increased.

Most current tumor imaging techniques are performed before surgery toprovide information about tumor location. Presurgery imaging methodsinclude magnetic resonance imaging (MRI) and computerized tomography(CT). In the operating room, only intraoperative ultrasound andstereotaxic systems can provide information about the location oftumors. Ultrasound shows location of the tumor from the surface but doesnot provide information to the surgeon once surgery begins to preventdestruction of important functional tissue while permitting maximalremoval of tumor tissue. Stereotaxic systems coupled with advancedimaging techniques have (at select few hospitals) been able to localizetumor margins based upon the preoperative CT or MRI scans. Howeverstudies (Kelly, 1990) have shown that the actual tumor extends 2-3 cmbeyond where the image enhanced putative tumor is located onpreoperative images. Therefore, the only current reliable method todetermine the location of tumors is by sending biopsies during surgery(i.e., multiple histological margin sampling) and waiting for results ofmicroscopic examination of frozen sections. Not only is it not advisableto continually take breaks during surgery, but such biopsies are, atbest, an estimation technique and are subject to sampling errors andincorrect readings as compared to permanent tissue sections that areavailable about one week later. Thus, a surgeon often relies upon anestimation technique as a guide when patient outcome is dependent uponaggressive removal of tumor tissue. Surgeons have difficult decisionsbetween aggressively removing tissue and destroying surroundingfunctional tissue and may not know the real outcome of their procedureuntil one week later and this may require an additional surgicalprocedure.

Multiple histological margin sampling suffers several drawbacks. Firstthis is a time-consuming procedure as it can add about 30 to 90 minutes(depending upon the number of samples taken) to a surgical procedurewhen the patient is under anesthesia. Second, this procedure is prone toerrors as a pathologist must prepare and evaluate samples in shortorder. Third, it is certainly the case that margin sampling does nottruly evaluate all regions surrounding a primary tumor as some areas ofresidual tumor can be missed due to sampling error. Fourth, increasedtime for margin sampling is expensive as operating room time costs arehigh and this leads to increased overall medical costs. Moreover,increased operating room time for the patient increases the probabilityof infection.

Other techniques developed to improve visual imaging of solid tumormasses during surgery include determining the shape of visibleluminescence spectra from normal and cancerous tissue. According to U.S.Pat. No. 4,930,516, in cancerous tissue there is a shift to blue withdifferent luminescent intensity peaks as compared to normal tissue. Thismethod involves exciting tissue with a beam of ultraviolet (UV) lightand comparing visible native luminescence emitted from the tissue with ahistorical control from the same tissue type. Such a procedure isfraught with difficulties because a real time, spatial map of the tumorlocation is not provided for the use of a surgeon. Moreover, the use ofUV light for an excitation wavelength can cause photodynamic changes tonormal cells, is dangerous for use in an operating room, and penetratesonly superficially into tissue and requires quartz optical componentsinstead of glass.

Therefore, there is a need in the art for a more comprehensive andfaster technique and a device for assisting such a technique to localizefor solid tumor locations and map precise tumor margins in a real-timemode during surgery. Such a device and method should be further usefulfor inexpensive evaluation of any solid tumor (e.g., breast mammography)by a noninvasive procedure and capable of grading and characterizing thetumors.

There is also a need to image brain functioning during neurosurgicalprocedures. For example, a type of neurosurgical procedure which alsoexemplifies these principles is the surgical treatment of intractableepilepsy (that is, epilepsy which cannot be controlled withmedications). Presently, electroencephalography (EEG) andelectrocorticography (ECoG) techniques are used prior to and duringsurgery for the purposes of identifying areas of abnormal brainactivity, such as epileptic foci. These measurements provide a directmeasurement of the brain's electrical activity.

Intraoperative EEG techniques involve placing an array of electrodesupon the surface of the cortex. This is done in an attempt to localizeabnormal cortical activity of epileptic seizure discharge. Although EEGtechniques are of widespread use, hazards and limitations are associatedwith these techniques. The size of the electrode surface and thedistance between electrodes in an EEG array are large with respect tothe size of brain cells (e.g., neurons) with epileptic foci. Thus,current techniques provide poor spatial resolution (approximately 1.0cm) of the areas of abnormal cortical activity. Further, EEG techniquesdo not provide a map of normal cortical function in response to externalstimuli (such as being able to identify a cortical area dedicated tospeech, motor or sensory functions by recording electrical activitywhile the patient speaks). A modification of this technique, calledcortical evoked potentials, can provide some functional corticalmapping. However, the cortical evoked potential technique suffers fromthe same spatial resolution problems as the EEG technique.

The most common method of intraoperative localization of corticalfunction in epilepsy and tumor surgery is direct electrical stimulationof the cortical surface with a stimulating electrode. Using thistechnique, the surgeon attempts to evoke either an observed motorresponse from specific parts of the body, or in the case of an awakepatient, to generate specific sensations or cause an interruption in thepatient's speech output. Again, this technique suffers from the sameproblems as the EEG technique because it offers only crude spatiallocalization of function.

Possible consequences of the inaccuracies of all these techniques, whenemployed for identifying the portion of the cortex responsible forepileptic seizures in a patient, are either a greater than necessaryamount of cortical tissue is removed possibly leaving the patient with adeficit in function, or that not enough tissue is removed leaving thepatient uncured by the surgery. Despite these inadequacies, suchtechniques have been deemed acceptable treatment for intractableepilepsy. The same principles apply to tumor surgeries, however,intraoperative functional mapping is not performed routinely.

In the past few years, researchers have been using imaging techniques inanimal models to identify functional areas of cortex with high spatialresolution. One type of such technique uses a voltage-sensitive dye. Avoltage-sensitive dye is one whose optical properties change duringchanges in electrical activity of neuronal cells. The spatial resolutionachieved by these techniques is near the single cell level. Blasdel andSalama (Nature 321:579, 1986) used a voltage-sensitive dye (merocyanineoxazolone) to map cortical function in a monkey model. The use of thesekinds of dyes would pose too great a risk for use in humans in view oftheir toxicity. Further, such dyes are bleached by light and must beinfused frequently.

Recently, measurement of intrinsic signals have been shown to providesimilar spatial resolution as voltage-sensitive dye imaging. Intrinsicsignals are light reflecting changes in cortical tissue partially causedby changes in neuronal activity. Unlike other techniques used forimaging neuronal activity, imaging intrinsic signals does not requireusing dyes (which are often too toxic for clinical use) or radioactivelabels. For example, Grinvald et al. (Nature 324:361, 1986) measuredintrinsic changes in optical properties of cortical tissue by reflectionmeasurements of tissue in response to electrical or metabolic activity.Light of wavelength 500 to 700 nm may also be reflected differentlybetween active and quiescent tissue due, to increased blood flow intoregions of higher neuronal activity. Another aspect which may contributeto intrinsic signals is a change in the ratio of oxyhemoglobin todeoxyhemoglobin.

Ts'o et al. (Science 249:417, 1990) used a charge-coupled device (CCD)camera to detect intrinsic signals in a monkey model. However, thistechnique would not be practical in a clinical environment becauseimaging was achieved by implanting a stainless steel optical chamber inthe skull and in order to achieve sufficient signal to noise ratios,Ts'o et al. had to average images over periods of time greater than 30minutes per image. By comparison to all other known techniques forlocalizing cortical function, imaging intrinsic signals is a relativelynon-invasive technique.

Mechanisms responsible for intrinsic signals are not well understood,possible sources of intrinsic signals include dilatation of small bloodvessels, increased scattering of light from neuronal activity-dependentrelease of potassium, or from swelling of neurons and/or glial cells.

Therefore, there is a need in the art for a procedure and apparatus forreal-time optical imaging of cortical tissue which can precisely andquickly distinguish normal and abnormal cortical tissue. There is also aneed in the art for developing a method that can image intrinsic signalswith high spatial resolution, provide immediate images and be compatiblewith normal procedures in the operating room. This invention was made,in part, in an effort to satisfy this need.

SUMMARY OF THE INVENTION

The inventive method and device can be used to identify, grade andcharacterize solid tumors by imaging changes in electromagneticabsorption which reflects dynamics of dye perfusion through tissue,wherein the inventive device is able to differentiate tumor tissue fromsurrounding normal tissue with dynamic changes in optical signals duringdye perfusion. Further, the inventive method and device can be used toidentify areas of neuronal activity during neurosurgical procedures. Inparticular, this invention can be used by a neurosurgeonintraoperatively to identify areas in the brain dedicated to importantfunctions such as vision, movement, sensation, memory and language.Further the present inventive method and device can be used to detectareas of abnormal cortical activity, such as epileptic foci. Lastly, thepresent invention can be used to identify individual nerves duringneurosurgical procedures for tumor removal or anastamoses of severednerves.

The present invention provides an apparatus for imaging tumor tissue orfor real-time surgical imaging of cortical intrinsic signals orvisualizing margins of solid tumor tissue from dynamic changes inoptical signals during dye perfusion, comprising, a means for obtaininga series of analog video signals, and a means for processing the analogvideo signals into either an averaged control image or a subsequentaveraged image, a means for acquiring and analyzing a plurality ofsubsequent images and averaged control images to provide a differenceimage, wherein the difference image is processed to account for movementand noise and to amplify the changes across a dynamic range of theapparatus, and a means for displaying the difference image alone orsuperimposed over an analog video image.

The present invention further provides a method for imaging tumormargins and dimensions of solid tumor tissue located in an area ofinterest, comprising illuminating the area of interest with spatiallyeven, intensive and non fluctuating light containing a wavelength ofelectromagnetic radiation (emr) (e.g., light) absorbed by a dye,obtaining a video signal of the area of interest as a series of framesand processing the series of frames into an averaged control image,administering the dye by bolus injection into vasculature circulating tothe area of interest, obtaining a subsequent series of frames of thearea of interest over time and processing the subsequent series offrames into a subsequent averaged image, comparing each subsequentaveraged image with the averaged control image to obtain a series ofdifference images, and comparing each difference image for initialevidence of changed optical signals within the area of interest which isthe outline of solid tumor tissue, wherein tumor tissue is characterizedby different kinetics of dye uptake compared to normal tissue and atemporally changed pattern of altered absorption of light as a result ofincreased vascularity of solid tumor tissue. Examples of appropriatedyes include indocyanine, fluorescein, hematoporphyrin, andfluoresdamine. A preferred dye is indocyanine green which has a broadabsorption wavelength range and a peak absorption in the range of 730 nmto 840 nm.

The present invention further comprises a method in real-time foroptically imaging functional areas of interest of the cortex in apatient comprising illuminating the area of interest with high intensityemr containing near-infrared wavelengths of emr, obtaining a series offrames of the area of interest and processing the series of frames intoan averaged control image, administering a stimulus paradigm to thepatient to stimulate an intrinsic signal, obtaining a series ofsubsequent frames of the area of interest over time and processing thesubsequent series of frames into a subsequent averaged image, comparingeach subsequent averaged image with the averaged control image to obtaina series of difference images, and comparing each difference image forinitial evidence of an intrinsic signal within the area of interest,whereby an intrinsic signal is characterized by a change in emrreflectance properties manifest as a signal in the difference image.

The present invention further includes a method for imaging damage to aperipheral or cranial nerves comprising: (a) illuminating an area ofinterest with high intensity emr, wherein the area of interest comprisesthe peripheral nerve of interest including the suspected site of damageand a region adjacent of the suspected site of damage; (b) obtaining aseries of frames of the area of interest and processing the series offrames into an averaged control image; (c) stimulating the peripheral orcranial nerve at a site adjacent of the suspected damaged site; (d)obtaining a series of subsequent frames at the time of stimulation andprocessing the series of subsequent frames into a subsequent averagedimage; and (e) obtaining a difference image by subtracting the averagedcontrol image from the subsequent averaged image to visualize the activeregion of the peripheral or cranial nerve, whereby nerve blockage isvisualized as the point along the nerve where the intrinsic signal fromthe stimulated nerve abruptly ends, or is altered, attenuated ordiminished in the difference image.

The present invention further includes a method for imaging tumor tissuesurrounding or adjacent to nerve tissue to aid in selective resection oftumor tissue without destroying nerve tissue, comprising: (a)illuminating an area of interest with high intensity emr containingwavelength of emr absorbed by a dye; (b) obtaining a series of frames ofthe area of interest and processing the series of frames into anaveraged control image; (c) stimulating the nerve; (d) obtaining aseries of subsequent nerve frames and processing the subsequent seriesof nerve frames into a subsequent nerve averaged image; (e) obtaining anerve difference image by subtracting the nerve averaged control imagefrom the nerve subsequent averaged image to visualize the active nerve;(f) administering a dye into an artery feeding the area of interest; (g)obtaining a series of tumor subsequent frames and processing the tumorsubsequent series of frames into a tumor subsequent averaged image; and(h) obtaining a tumor difference image by subtracting the tumor averagedcontrol image from the tumor subsequent averaged image to create a tumordifference image that is capable of visualizing the tumor. Further, thetumor difference image and the nerve difference image can besuperimposed upon each other to simultaneously visualize the relativelocations of tumor tissue and nervous tissue.

The present invention further comprises a method for enhancingsensitivity and contrast of the images obtained from tumor tissue orintrinsic signal difference images, comprising: (a) illuminating an areaof interest with a plurality of wavelengths of emr, wherein there is atleast a first wavelength of emr and a second wavelength of emr; (b)obtaining a sequence of frames corresponding to each wavelength of emr,wherein a first sequence of frames is from the first wavelength of emr,the second sequence of frames is from the second wavelength of emr andso on; (c) processing the first sequence of frames into a first averagedcontrol image, the second sequence of frames into a second averagedcontrol image and so on; (d) stimulating for intrinsic signals oradministering a dye for tumor tissue imaging; (e) obtaining a firstseries of subsequent frames using the first wavelength of emr, a secondseries of subsequent frames using the second wavelength of emr, and soon, and processing the first, second and so on subsequent series offrames into the first, second and so on subsequent averaged images,respectively; (f) obtaining a first difference image by subtracting thefirst averaged control image from the first subsequent averaged imageand a second difference image by subtracting the second averaged controlimage from the second subsequent averaged image, and so on; and (g)obtaining an enhanced difference image by ratioing the first differenceimage to the second difference image. Preferably, the monochromatic emrsources to illuminate the area of interest are from laser sources. Thistechnique is useful for obtaining three dimensional information of thearea of interest.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGs. 1A-D illustrate a view of human cortex just anterior to face-motorcortex with one recording (r) and two stimulating electrodes (s), andthree sites (#1, #2, #3) where average percent changes were determined.The scale bar equals 1 cm. Averages of 128 images (4/sec) were acquiredat 30 Hz and stored (1/sec). After acquiring 3-6 averaged control images(5 sec/image), a bipolar cortical stimulation evoked epileptiformafterdischarge activity.

FIG. 1A is a view of a human cortex just anterior to face-motor cortexwith one recording electrode (r) and two stimulating electrodes (s), andfour sites (the boxed areas labeled 1, 2, 3, and 4) where the averagepercent changes of absorption over these areas were determined. Thecortex was illuminated with emr >690 nm. The scale bar is 1 cm.

FIG. 1B are plots of the percent change of emr absorption per second inthe spatial regions of boxes 1 and 3 (as labeled in FIG. 1A). For bothregions, the peak change is during the fourth stimulation trial (at 8mA) in which the greatest amount of stimulating current had induced themost prolonged epileptiform afterdischarge activity. The changes withinbox 3 were greater and more prolonged than those of box 1. Box 3 wasoverlying the area of the epileptic focus (the excitable area of tissuepossibly responsible for the patient's epilepsy).

FIG. 1C show plots of the percent change of emr absorption per second inthe spatial regions of boxes 1 and 4 (as labeled in FIG. 1A). Box 1overlays and area of cortical tissue between the two stimulatingelectrodes, and box 4 overlays a blood vessel. The changes within box 4are much larger and in the opposite direction of box 1. Also thesechanges are graded with the magnitude of stimulating current andafterdischarge activity. Since the changes in box 4 are most likely dueto changes of the blood-flow rate within a blood vessel, this plot showsthat the invention can simultaneously monitor cortical activity andblood-flow.

FIG. 1D shows plots of the percent change of emr absorption per secondin the spatial regions of boxes 1 and 2 (as labeled in FIG. 1A). Notethat although these two areas are nearby each other, their opticalchanges are in the opposite direction during the first three stimulationtrials using 6 mA current. The negative going changes within the regionof box 2 indicate that the invention may be used to monitor inhibitionof cortical activity as well as excitation.

FIG. 2 illustrates a spatial map of stimulation induced epileptiformactivity. This Figure shows a comparison between different degrees ofactivation for both spatial extent and amplitude of optical changegraded with the extent of cortical activity. Specifically, FIG. 2 showspercentage difference images from various times during two stimulationtrials (definition of stimulation trial is given in description ofFIG. 1) from those described in FIG. 1. The top 3 pictures (A2, B2, andC2) are from stimulation trial 2 where 6 mA cortical stimulation evokeda brief period of afterdischarge. These are compared to the bottom threepictures (A4, B4, and C4) which are from stimulation trial 4 showing theoptical changes evoked by cortical stimulation at 8 mA. FIGS. 2, A2 andA4 compare control images during rest. FIGS. 2, B2 and B4 compares thepeak optical changes occurring during the epileptiform afterdischargeactivity. FIGS. 2, C2 and C4 compares the degree of recovery 20 secondsafter the peak optical changes were observed. The magnitude of opticalchange is indicated by the gray-scale bar in the center of the Figure.The arrow beside this gray-scale indicates the direction of increasingamplitude. Each image maps an area of cortex approximately 4 cm by 4 cm.

FIG. 3 shows a sequence of dynamic changes of optical signalsidentifying active areas and seizure foci. This Figure shows eightpercentage difference images from the stimulation trial 2 described inthe previous two Figures. Each image is integrated over a two secondinterval. The focal area of greatest optical change is in the center ofimages 3, 4, and 5, indicating the region of greatest cortical activity.This region is the epileptic focus. The magnitude of optical change isindicated by the gray-scale bar on the right side of the Figure. Thearrow beside this gray-scale indicates the direction of increasingamplitude. Each image maps an area of cortex approximately 4 cm by 4 cm.

FIG. 4 illustrates a real-time sequence of dynamic changes ofstimulation-evoked optical changes in human cortex. FIG. 4, panels 1through 8, show eight consecutive percentage difference images, eachimage is an average of 8 frames (<1/4 second per image). The magnitudeof optical change is indicated by the gray-scale bar in the center ofthe Figure. The arrow beside this gray-scale indicates the direction ofincreasing amplitude. Each image maps to an area of cortex that isapproximately 4 cm by 4 cm. This Figure demonstrates that the inventivedevice and method can be used to map, in real time, dynamics of opticalchanges and present such information to the surgeon in an informativeformat.

FIG. 5 shows an activation of somatosensory cortex by stimulation of aperipheral nerve in an anesthetized rat (afferent sensory input bydirectly stimulating the sciatic nerve in the hind limb of a rat). Theleftmost image is a gray-scale image of hind limb somatosensory cortexin an anesthetized rat. The magnification is sufficiently high so thatindividual capillaries can be distinguished (the smallest vesselsvisible in this image). The center image is an image of a percentagedifference control optical image during rest. The magnitude of opticalchange is indicated by the gray-scale bar in the center of this image.The arrow beside this gray-scale indicates the direction of increasingamplitude. The rightmost image is a percentage difference map of theoptical changes in the hind limb somatosensory cortex during stimulationof the sciatic nerve.

FIG. 6 shows functional mapping of human language (Broca's area) andtongue and palate sensory areas in an awake human patient. During three"tongue wiggling" trials images were averaged (32 frames, 1 sec) andstored every 2 seconds. A tongue wiggling trial consisted of acquiring5-6 images during rest, then acquiring images during the 40 seconds thatthe patient was required to wiggle his tongue against the roof of hismouth, and then to continue acquiring images during a recovery period.The same patient engaged in a "language naming" trial. A language namingtrial consisted of acquiring 5-8 images during rest (control images--thepatient silently viewing a series of blank slides), then acquiringimages during the period of time that the patient engaged in a namingparadigm (naming a series of objects presented with a slide projectorevery 2 seconds, selected to evoke a large response in Broca's area),and finally a series of images during a recovery period following a timewhen the patient ceased his naming task (again viewing blank slideswhile remaining silent). Images A1 and B1 are gray-scale images of anarea of human cortex with left being anterior, right-posterior,top-superior, and the Sylvan fissure on the bottom. The two asterisks onA1, B1, A2, and B2 serve as reference points between these images. Thescale bars in the lower right corner of A1 and B1 are equal to 1 cm. InA1, the numbered boxes represent sites where cortical stimulation withelectrical stimulating electrodes evoked palate tingling (1), tonguetingling (2), speech arrest-Broca's areas (3,4) and no response (11, 12,17, 5-premotor). Image A2 is a percentage difference control image ofthe cortex during rest in one of the tongue wiggling trials. Thegray-scale bar on the right of A2 shows the relative magnitude of thecolor code associated with images A2, A3, B2 and B3. Image A3 is apercentage difference map of the peak optical changes occurring duringone of the tongue wiggling trials. Areas identified as tongue and palatesensory areas by cortical stimulation showed a large positive change.Suppression of baseline noise in surrounding areas indicated that,during the tongue wiggling trials, language-motor areas showed anegative-going optical signal. Image B2 is percentage difference controlimage of the cortex during one of the language naming trials. Image B3is a percentage difference image of the peak optical change in thecortex during the language naming task. Large positive-going signals arepresent in Broca's area. Negative-going signals are present in tongueand palate sensory areas.

FIG. 7 shows a time course and magnitude plot of dynamic optical changesin human cortex evoked in tongue and palate sensory areas and in Broca'sarea (language). This Figure shows the plots of the percentage change inthe optical absorption of the tissue within the boxed regions shown inFIG. 6, images A1 and B1, during each of the three tongue wigglingtrials and one of the language naming trials (see, description of FIG.6). Diagram 7A shows the plots during the three tongue wiggling trialsaveraged spatially within the boxes 1, 2, 3, and 4 as identified in FIG.6, image A1. Diagram 7B shows the plots during one of the languagenaming trials averaged spatially within the boxes 1-7 and 17.

FIG. 8 illustrates an optical map of a cortical area important forlanguage comprehension (Wernicke's area) in an awake human. FIG. 8 imageA shows the cortical surface of a patient where the anatomicalorientation is left-anterior, bottom-inferior, with the Sylvan fissurerunning along the top. After optical imaging, all cortical tissue to theleft of the thick line was surgically removed. Sites #1 and #2 wereidentified as essential for speech (e.g., cortical stimulation blockedability of subject to name objects). At site #3, one naming error in 3stimulation trials was found. As the surgical removal reached the arealabeled by the asterisks on the thick line, the patient's languagedeteriorated. All the unlabeled sites in FIG. 8A had no errors whilenaming slides during cortical stimulation. FIG. 8, image B shows anoverlay of a percentage difference image over the gray-scale image ofthe cortex acquired during a language naming trial (see, FIG. 6 fordescription of the language naming trial). The magnitude of the opticalchange is shown by the gray-scale bar on the lower right of the image.This image demonstrates how a surgeon might use this inventionintraoperatively to map language cortex.

FIG. 9 illustrates a timecourse and magnitude of dynamic optical changesin human cortex evoked in Wernicke's area (language comprehension). FIG.9A shows plots of percentage change in optical absorption of tissuewithin the boxed regions shown in FIG. 8. The plots of boxes 1 and 2overlay essential language sites, and boxes labeled 4, 5, and 6 overlaysecondary language sites. Each of these five sights showed significantchanges occurring while the patient was engaged in a language namingtask. FIG. 9B show percentage changes from the six unlabeled boxes shownin FIG. 8. There were no significant increases or decreases within theseanterior sites.

FIG. 10 illustrates differential dynamics of dye to identify low gradehuman CNS tumor. This series of images are from a patient having a lowgrade CNS tumor (astrocytoma, grade 1). In FIG. 10A (upper left) thelettered labels placed upon the brain by the surgeon overlay the tumoras identified intraoperatively by ultrasound. However, tumors of thistype and grade are notoriously difficult to distinguish from normaltissue once the surgical removal of the tumor has begun. FIG. 10B(middle left) shows a difference image taken approximately 15 secondsafter intravenous injection of dye (indocyanine green at 1 mg/kg). FIG.10C (lower left) shows the difference image about 30 seconds after dyeadministration. The area of the tumor tissue showed the first tissuestaining. FIG. 10D (top right) shows that in this low grade tumor, alltissue (both normal and abnormal) showed staining at 45 sec after dyeadministration. FIG. 10E (middle right) is one minute after dyeadministration and FIG. 10F is five minutes after dye administration(showing complete clearance in this low grade tumor). These data showthat indocyanine green enters low grade tumor tissue faster than normalbrain tissue, and may take longer to be cleared from benign tumor tissuethan normal tissue, providing utility to image even low grade tumors,and to distinguish intraoperatively, low grade tumor tissue fromsurrounding normal tissue.

FIG. 11 illustrates that differential dynamics of dye identify malignanthuman CNS tumor. The series of images in this Figure are from the cortexof a patient with a malignant CNS tumor (glioblastoma; astrocytoma,Grade IV). FIG. 11A (upper left) shows a gray-scale image in whichmalignant brain tumor tissue was densest in the center and to the rightbut elsewhere was mostly normal tissue (as was shown by pathology slidesand flow cytometry available one week after surgery). FIG. 11B (middleleft) is the difference image at 15 seconds after intravenous injectionof indocyanine green, showing the dynamics of dye perfusion in the firstseconds in malignant tissue are similar to those in the first fewseconds of benign tumor tissue (see FIG. 11C). FIG. 11C (lower left)shows that at 30 seconds the malignant tissue is even more intense bycomparison to the normal tissue. FIG. 11D (upper right, 1 minute afterdye injection) and 11E (lower right, 10 minutes after dye injection)show that unlike benign tumor tissue, in malignant tumor tissue, dye isretained significantly longer, and in some cases, continues to sequesterin the malignant tumor tissue over longer periods of time. These dataillustrate a utility to identify malignant tumor tissue, distinguishintraoperatively between normal and malignant tumor tissue, and todistinguish between the various grades of tumor (e.g., normal vs. benignvs. malignant).

FIG. 12 shows that differential dynamics of dye identify small remnantsof tumor tissue in the margin of a resected malignant human CNS tumor.The images are from an area of interest where a tumor was surgicallyresected and biopsies were taken for multiple histological marginsampling. The area of interest was thought to be free of tumor tissueafter the surgical removal of the tumor. Normally, in this size of aresection margin, only a single frozen sample would be taken forpathology analysis. For the purpose of this study, five biopsies weretaken from the margin to aid in correlating the histology with the mapobtained by the invention. FIG. 12A (top left) shows a gray-scale imageof the tumor margin. FIG. 12B shows the margin with labels that thesurgeon placed directly on brain. The purpose of these labels were toidentify where the surgeon was going to remove biopsy samples forhistological analysis after difference images were acquired with theinventive device. FIG. 12C (lower left) shows the difference image 1minute after intravenous injection of dye and FIG. 12D (lower right)shows the difference image 10 minutes after dye injection. Thesepost-dye difference images reveal a number of sights that contain tumortissue as well as areas of normal tissue. The accuracy of the opticalimaging was confirmed post operatively by analysis of the biopsies. Notethat a small area on the lower right of FIG. 12D indicates a possibleregion of tumor tissue that would not have been biopsied by the surgeon.Hence, even in the case of extensive biopsy, the sampling error exceedsthe accuracy of the invention. These data show a utility to identifysmall remnants of tumor tissue in a tumor margin after resection of atumor.

FIG. 13 shows that differential dynamics of dye can identify andcharacterize tumors in human patients that do not contrast enhance withMRI imaging. A proportion of non-benign tumors are not observable withpresent MRI imaging techniques. The images in this Figure are from apatient whose tumor did not contrast enhance with MRI. This lack ofenhancement is usually typical of benign tumors. However, opticalimaging was able to identify this tumor as a non-benign type (ananoplastic astrocytoma as shown one week later by pathology and flowcytometry). FIG. 13A shows the gray-scale image of the area of interest.FIG. 13B shows the difference image prior to dye injection. FIG. 13Cshows the area of interest 1 minute after intravenous dye injection, andFIG. 13D shows the area of interest 5 minutes after dye injection. Notethat the dye is retained in this tissue for a significant time. As shownin FIGS. 10, 11, and 12, this dynamic trait is a characteristic of anon-benign tumor.

FIG. 14 shows non-invasive imaging of dye dynamics and identification ofglioma through the intact cranium. This figure demonstrates that theinvention can be used to identify tumors through the intact cranium.FIG. 14A is a gray-scale image of the cranial surface of a rat. Thesagital suture runs down the center of the image. Tumor cells had beeninjected into the left side some days earlier so that this animal haddeveloped a glioma on the left hemisphere of its brain. The righthemisphere was normal. Box 1 lays over the suspect region of braintumor, and box 2 lays over normal tissue. FIG. 14B is a difference image1 second after indocyanine green dye had been intravenously injectedinto the animal. The region containing tumor tissue became immediatelyvisible through the intact cranium. FIG. 14C shows that 5 seconds afterdye injection the dye can be seen to profuse through both normal andtumor tissue. FIG. 14D shows that 1 minute after dye injection, thenormal tissue had cleared the dye, but dye was still retained in thetumor region. The concentration of dye in the center of this differenceimage was dye circulating in the sagital sinus.

FIG. 15 illustrates dynamic information of dye uptake and clearance intumor vs. non-tumor tissue through the intact skull. This is a plot ofan average of the percentage change in emr absorption average over thespatial areas indicated by boxes 1 and 2 from FIG. 14A. The increase inabsorption was a function of the concentration of dye in the tissue at aparticular time. The graph labeled "extracranial tumor" is a plot of thedynamics of the absorption changes within box 1 from FIG. 14A. The graphlabeled "extracranial: normal" is a plot of the dynamics of theabsorption change within box 2 from FIG. 14A.

FIG. 16 shows a spatial map of dynamic changes in tumor vs. non-tumorareas in the rat glioma model. The sequence of images in this figuredemonstrate the dynamic differences of the absorption changes due to dyebetween tumor and non-tumor tissue. FIG. 16A shows a gray-scale image ofthe area of interest. This is the same animal as shown in FIG. 14,however the cranium has now been removed so as to expose the lefthemisphere containing the glioma, and the right hemisphere containingnormal tissue. Box 1 overlays the tumor, Box 2 the tumor-surround, andBox 3 overlays normal tissue. FIG. 16B shows the difference image of thearea of interested 1 second after 1 mg/kg of indocyanine green had beenintravenously injected into the animal. During this initial time, thetumor tissue was the first to show a measurable optical changeindicating the uptake of dye occurs first in the tumor tissue. Thegray-scale bar indicated the relative magnitude of the optical changesin the sequence of difference images. FIGS. 16C and 16D show differenceimages of the area of interest 4 seconds and 30 seconds, respectively,after dye injection. At these intermediate stages dye appears to collectin both normal and tumor tissue. FIGS. 16E and 16F show differenceimages of the area of interest 1 minute and 5 minutes, respectively,after injection of dye. At these later times, it becomes clear that dyewas still collecting in tumor tissue even thought it was being clearedfrom normal tissue.

FIG. 17 shows dynamic information of dye uptake and clearance in tumorvs. non-tumor tissue. This is a plot of an average of the percentagechange in emr absorption averaged over the spatial areas indicated byboxes 1, 2, and 3 from FIG. 16A. The increase in absorption was afunction of the concentration of dye in the tissue at a particular time.The graph labeled "tumor tissue" is a plot of the dynamics of theabsorption changes within box 1 from FIG. 16A. The graph labeled "tumorsurround" is a plot of the dynamics of the absorption changes within box2 from FIG. 16A. The graph labeled "normal brain" is a plot of thedynamics of the absorption changes within box 3 from 16A

FIG. 18 shows dynamic imaging of dye uptake reveals residual traces oftumor cells in resected tumor margins. This is a continuation of thestudy on the same animal shown in FIGS. 14 through 17. FIG. 18A shows ahigher magnification image of the left hemisphere tumor margin of theanimal after the tumor has been resected. Boxes 1 are over areas thatcontain small traces of residual tumor cells, and boxes 2 are over areascontaining only normal tissue. The gray-scale bar indicates themagnitude of optical change in the difference images. FIGS. 18B, 18C,and 18D show difference images of the tumor margin 4, 30, and 60 secondsafter intravenous dye injection respectively. Minute biopsies were takenfrom areas that showed preferred dye containment and from areas fromwhich the dye cleared rapidly. These biopsies were analyzed blindly andlater correlated to the location from which the biopsies were taken.Those biopsies taken from areas which cleared dye were shown to containonly normal cells, whereas biopsies taken from areas which sequestereddye were shown to contain tumor cells. Extremely small islands ofresidual tumor can be mapped within the tumor margins.

FIG. 19 shows dynamic information of dye uptake and clearance in tumorvs. non-tumor tissue. This is a plot of an average of the percentagechange in emr absorption average over the spatial areas indicated byboxes 1 and 2 from FIG. 18A. The increase in absorption is a function ofthe concentration of dye in the tissue at a particular time. The graphlabeled "margins tumor" is a plot of the dynamics of the absorptionchanges within box 1 from FIG. 18A. The graph labeled "margins normal"is a plot of the dynamics of the absorption changes within box 2 fromFIG. 18A. This data as well as that from FIG. 18 show that the inventivedevice and method are able to distinguish tumor from non-tumor tissuewithin tumor margins with extremely high spatial and temporalresolution.

FIG. 20 illustrates a view of hind limb somatosensory cortex in ananesthetized rat to demonstrate measurement of blood flow rates withinvessels of diameters as small as 2 micrometers in accordance with thepresent invention. FIG. 20A shows a gray-scale image mapping an area ofa rat cortex that is approximately 1 mm by 1 mm showing exemplary dataacquisition boxes 1, 2, and 3 encompassing an arterial, a venule, andcortical tissue, respectively. FIG. 20B shows plots of percentage changeof emr absorption per second in the spatial regions of boxes 1, 2, and 3and a plot of corresponding morphological measurements of the venule inthe spatial region of box 2. FIG. 20C is a sequence of pseudocolorimages showing dynamic changes of optical signals corresponding to bloodflows plotted in FIG. 20B. FIG. 20D is a pair of pseudocolor imagesformed by converse subtractive calculations to show the opposite changesof optical signals corresponding to arterials and venules.

FIGS. 21A-D illustrates a view of human cortex just anterior toface-motor cortex with one recording (r) and two stimulating electrodes(s). Each image maps to an area of cortex that is approximately 4 cm by4 cm. FIGS. 21B-21E each corresponds to an average of approximately 60frames which were acquired at 30 Hz over a period of about 2 seconds.The cortex was illuminated with emr of wavelengths greater than about690 nm and FIGS. 21B-21E represent changes in absorption of the emr overdifferent periods. Regions colored red, blue, and black correspond toincreasing (positive-going), decreasing (negative-going), andnon-changing levels of cortical activity, respectively.

FIG. 21A is a grey-scale image of a human cortex just anterior toface-motor cortex with two stimulating electrodes (s) for applyingstimulating current induce epileptiform afterdischarge activity and onerecording electrode (r) for obtaining surface electrical signals byconventional electroencephalography (EEG) techniques.

FIG. 21B is a spatial map of baseline cortical activity prior toapplication of stimulating current for inducing epileptiformafterdischarge activity. FIG. 21C is a spatial map of cortical activityduring stimulation at stimulating electrodes (s) and the resultingepileptiform afterdischarge activity. FIG. 21D is a spatial map ofcortical activity during an apparent quiescent period following theepileptiform afterdischarge activity induced by stimulation atstimulating electrodes (s). FIG. 21E is a spatial map of corticalactivity of a period following the quiescent period represented by FIG.21D.

FIG. 22 is a trace of an EEG recording of surface electrical signalsreceived by recording electrode (r) shown in FIG. 21A and correspondingto the baseline cortical activity of FIG. 21B (period A), the corticalactivity during stimulation and the resulting epileptiformafterdischarge activity of FIG. 21C (period B), the quiescent corticalactivity following the epileptiform afterdischarge activity of FIG. 21D(period C), and the subsequent cortical activity of FIG. 21E (period D).

FIGS. 23A1-B2 shows functional mapping of human language (Broca's area)and tongue and palate sensory areas in an awake human patient. FIGS.23A1 and 23B1 are gray-scale images of an area of human cortex with leftbeing anterior, right-posterior, top-superior, and the Sylvan fissure onthe bottom. The numeral 34 in FIG. 23A1 (partly obscured) serves asreference point to FIG. 23B1 in which the numeral is mostly obscured atthe upper right edge of the Figure. Each image maps to an area of cortexthat is approximately 4 cm by 4 cm. FIG. 23A2 and 23B2 are spatial mapsof cortical activity in the areas of human cortex shown in FIGS. 22A1and 22B1 during, respectively, a language naming exercise and a tonguewiggling exercise.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides an apparatus for imaging neuronalintrinsic signals in real time and for determining the presence, size,margins, dimensions, and grade of a solid tumor mass using a dye. Thepresent invention further provides a method for functional mapping ofthe cortex in a patient by mapping intrinsic signals in real time, amethod for determining the presence, size, location, and grade of solidtumor tissue in real time without the sampling errors of biopsies or thedelay of and possible misdiagnosis of the pathologist's frozen sectionanalysis, and a method for imaging nerve tissue that may be physicallydamaged or surrounded by and adjacent to tumor cells. The inventivemethods employ a similar apparatus, comprising a series of components,including video input hardware and dedicated image processing hardware.The video input hardware is, for example, a photo-detector, such as aCCD (charge coupled device) camera (preferably a COHU 6510 CCDMonochrome Camera with a COHU 6500 electronic control box made by COHUElectronics San Diego, Calif.). In some cameras the analog signal isdigitized 8-bits deep on an ADI board (analog-to-digital board). Thededicated image processing hardware is generally controlled by a "hostcomputer". The host computer is any common general computer (such as anIBM PC type with an Intel 386, 486 or better microprocessor or SunSPARC) that is interfaced with the dedicated imaging hardware and sendscommands to the imaging hardware that direct data flow, computations,image acquisition and the like. Thus, the host computer directs theactions of the imaging hardware and provides the user interface.

Definitions

The following are definitions of commonly used terms and that areapplied in this application according to their art-accepted usage, suchas described in Inoue, Video Microscopy Plenum Press, New York, 1989.

Area of Interest is that area of tissue that comprises the subject ofthe image.

Arithmetic Logic Unit (ALUM is the hardware component that performs avariety of mathematical and logic operations (e.g., sum, difference,exclusive or, multiply by a constant, etc.) on the image signal atextremely high speeds.

Averaged Control Image is that updateable image that is the average of aseries of real time images over a period of time.

Charge Coupled Device (CCD) is a photo-sensitive silicon chip used inplace of a pickup tube in miniature video cameras.

Difference Image is the manipulated image created by adding orsubtracting a subsequent image or a particular image in time from anaveraged control image.

Frame is a single digitized array of single video pictures.

Frame Buffer is a piece of hardware that serves as a temporary storageof a frame, such as an averaged control image, a subsequent image or adifference image.

Geometric Transformation (Gonzalez and Wintz, Digital Image Processing,Addison-Wesley Publishing Co., Reading, 1987) generally modify spatialrelationships between pixels in an image. For this reason, geometrictransformations are often called "rubber sheet transformations" becausethey can be viewed as the process of "printing" an image on a sheet ofrubber and stretching this sheet according to a predefined set of rules.As applied to video imaging, subsequent images can be viewed as havingbeen distorted due to movement and it is desirable to "warp" theseimages so that they are similar to the control images. Geometrictransformations are distinguished from "point transformations" in thatpoint transformations modify a pixel's value in an image based solelyupon that pixel's value and/or location and no other pixel values areinvolved in the transformation.

Image is a frame or composition of frames that have been altered afterdigitization, such as processing a sequence of frames into an averagedcontrol image or a subsequent averaged image.

Intrinsic Signal means a detectable change in reflectance properties ofneuronal tissue due to endogenous physiologic activity. Possible causesof intrinsic signals include, for example, membrane depolarization,glial cell swelling, ion flux across neuronal membranes, blood volumechanges, blood deoxygenation (hemoglobin to deoxyhemoglobin), tissueoxygenation and combinations thereof.

Linear Histogram Stretch is a transformation in which the values betweentwo points (high, low) are remapped to cover a full range of values(i.e., dynamic range). For example, the low value is mapped to zero, thehigh to 255, and the intermediate values are mapped to linearlyincreasing brightness values. All brightness values below the low valueare set to zero and all brightness values above the high value are setto the high value.

Look Up Table (LUT) is a piece of hardware that functions to storememory that directs conversion of the gray value of each pixel intoanother gray value or color that is specified by the LUT. The LUT can beprogrammed to manipulate image contrast, threshold an image, applypseudocolor and the like (such as a convenient implementation method forpoint processing algorithms). In the case of the present invention, theLUTs are, preferably, implemented for speed on an ADI and/or ALU boards.

Paradigms cause a change in electrical activity of an area of corticaltissue dedicated to a specific function (e.g., speech, language, vision,etc.) thus causing an increase or decrease in what is called anintrinsic signal.

Pixel is the individual units of image in each frame of the digitizedsignal. The intensity of each pixel is linearly proportional to theintensity of illumination before signal manipulation and corresponds tothe amount of emr (photons) being reflected from a particular area oftissue corresponding to a particular pixel. It should be noted that animage pixel is the smallest unit of a digital image and its outputintensity can be any value. A CCD pixel is the smallest detectingelement on a CCD chip and its analog output is linearly proportional tothe number of photons it has detected.

Processed Difference Image is the raw difference image that has beenprocessed or manipulated to filter out noise or movement and increasethe dynamics of effect of different pixel values to illustrate events inthe area of interest.

Tumor Margin is the area where the surgeon has resected the tumor.

Apparatus

The inventive apparatus is made as one unit or a group of components.The first component is a high intensity emr source. The emr source isfor illuminating the cortical surface or area of interest, such as anarea suspected of having solid tumor tissue. Different intrinsic signalscan be illuminated by different wavelengths of emr. Moreover, the emrsource must include the wavelengths of emr absorbed by the dye for thetumor imaging method. For example, when the dye is indocyanine green,preferred wavelengths are from about 730 nm to about 840 nm. For otherdyes, the preferred wavelengths of illuminating emr should includewavelengths at which the dye absorbs. The term emr instead of light isused because it is also possible to image in the infrared region of thespectrum outside of the visible light range.

When determining intrinsic signals from the cortex, reflected emr can befiltered to allow for video imaging of only selected wavelengths of emr.Preferred selected wavelengths of emr include, for example, from about500 nm to about 900 nm, or most preferably, the near infrared spectrum.Generally, longer wavelengths (e.g., approximately 800 nm) measuredeeper cortical activity.

Moreover, that part of the infrared spectrum in an invisible range ofbetween 0.75 to about 1000 micrometers allows for a determination ofintrinsic signals through dura and skull, thereby allowing for adetermination of intrinsic signals through intact skull and dura andwithout the risks associated with neurosurgery. When using this range offar infrared wavelengths, an IR detector is a different device than aCCD chip for a visible analog camera. IR detectors are made frommaterials such as indium arsenide, germanium and mercury cadmiumtelluride rather than silicon. IR detectors must be cryogenically cooledin order that they be sensitive to small changes in temperature. Forexample, one IR imaging system is an IRC-64 infrared camera (CincinnatiElectronics, Mason, Ohio).

As heat reaches the surface of the cortex, it emits electromagneticradiation in the range of about 3-5 or 8-14 microns. Others haveattempted to image this emitted radiation (see, for example, Gorbach etal., "Infrared Mapping of the Cerebral Cortex" Thermography 3:108,1989). However, according to the present invention these emittedwavelengths are filtered out and an IR detector instead of a CCDdetector is used. An IR emr source is, for example, a Tunable IR DiodeLaser from Laser Photonics, Orlando, Fla. The imaged wavelengths aredifferent from body heat and images of changes in absorption and emrscattering can be obtained according to the inventive method. In thecase of tumor images through intact skin and possibly bone, a dye thatabsorbs in the IR can be used (e.g., indocyanine green). Other usefuldyes include, for example, Photofrin® derived from a hematoporphyrinderivative (HPD) and absorbs light at 630 nm, mono espatyl chlorin-36(NPe₆, Nippon Petrochemical, Japan), benzoporphyrin derivative (BPD,Quadra Logic Vancouver BC), Evans Blue, and combinations thereof.

Preferably, the emr source is a high intensity, broad spectrum emrsource, such as a tungsten-halogen lamp and a cutoff filter for allwavelengths below 695 nm. Most preferably, the emr source is directed tothe area of interest by a fiber optic means. An example of such a emrsource is a fiber optic emr passing through a beam splitter, controlledby a D.C. regulated power supply (Lambda, Inc.) and passed through a 695nm longpass filter.

The inventive apparatus includes a means for obtaining an analog videosignal of the cortex or area of interest. A preferred device forobtaining an analog video signal is a charge coupled device (CCD) videocamera which creates an output video signal at 30 Hz having, forexample, 512 horizontal lines per frame using standard RS 170convention. One such device is a CCD-72 Solid State Camera (Dage-MITInc., Michigan City, Ind.) and another such device is a COHU 6500 (COHU,San Diego Calif.).

The area of interest must be evenly illuminated to better adjust thesignal over a full dynamic range. If there is uneven illumination in thearea of interest, it will limit the dynamic range. Preferably a highintensity and diffuse or even lighting system is used. Techniques toobtain even illumination over the area of interest include, for example,diffuse lighting, image processing algorithms to compensate for unevenillumination on a digitized image, a constant shade gray image markerpoint in the area of interest as a control point, a wavelength cutofffilter in front of the camera and/or emr source, or combinationsthereof. Preferably, a regulated power supply will prevent fluctuationsin emr sources. A footplate system is an optical glass (sterile)contacting and covering the area of interest to provide a flattercontour. The footplate also retards tissue movement.

The analog signal must first be adjusted to maximize sensitivity ofdetection (at the level of the analog signal and before digitizing) toamplify the signal and spread the signal across the full possibledynamic range, thereby increasing sensitivity of the apparatus. 60 Hznoise (such as from A.C. power lines) is filtered out in the cameracontrol box by an analog filter. Such adjustments further serve toenhance, amplify and condition the analog signal from the CCD. One meansfor properly adjusting the input analog signal is to digitize thissignal at video speed (30 Hz), and view the area of interest as adigitized image that is converted back to analog.

It is important to compensate for small movements of tissue or thepatient during the imaging process. Larger patient movements require anew orientation of the camera and obtaining a new averaged controlimage. Compensating for movement can be done by mechanical orcomputational means or both. Mechanical means include, for example,placing a footplate over the area of interest wherein the footplatecomprises sterilized optical quality glass in a framing device, and/orsecuring the camera and possibly the emr source to the skeletal frame ofthe patient, and combinations of both. When the camera and/or emr sourceare attached to the skeletal structure of the patient, any patientmovements will not effect the image because the camera and illuminationsource will remain in a constant orientation to the area of interest.The advantage of the footplate is that it retards tissue movement causedby arterial pressure and/or respiration and prevents changes due toevaporation of cerebrospinal fluid. Computational means include, forexample, using functional control points in the area of interest andtriangulation-type algorithms to compensate for movements of thesecontrol or tie points, and "image warping" techniques whereby eachsubsequent image is registered geometrically to the averaged controlimage to compensate for movement, and combinations of both techniques.The image warping technique is described in, for example, Wolberg,"Digital Image Warping" IEEE Computer Society Press, Los Alimitos,Calif. 1990. The image warping technique can further indicate whenmovement has become too great for the averaged control image and that anew averaged control image must be taken. Control points can be placeddirectly in the area of interest, such as directly on the corticalsurface for intrinsic signal analysis. For example, Goshtasby("Piecewise Linear Mapping Functions for Image Registration" in PatternRecognition vol. 19 pp 459-66, 1986) describes a method whereby an imageis divided into triangular regions using control points. A separategeometrical transformation is applied to each triangular region tospatially register each control point to a corresponding triangularregion in a control image.

If the two images (averaged control image and subsequent image) aremisaligned prior to subtraction, artifacts will result since thedifference image will be more like a gradient image amplifying noise andedge information. Image misalignment can arise from patient motion,heartbeat and respiration. One solution is to fix the camera to a rigidassembly connected to the patient, such as his or her head such that anypatient motion also moves the camera's field of view accordingly.Another solution is to perform real time motion compensation with motiondetection and geometric transformation with the image processing board.Simple translation or more complicated (thus more accurate) unwarpingcan be implemented depending upon the input frame rate and amount ofaveraging.

In the case of imaging tissue (either for neuronal activity or fordynamical imaging of dye flow through tissue) in a human subject, it isnecessary to compensate for the motion of the subject which may occurbetween the acquisition of consecutive images. For many types of images,it is possible to compensate by a geometrical compensation whichtransforms the image by translation in the x-y plane. In order for analgorithm such as this to be feasible, it must be computationallyefficient (preferably implementable in integer arithmetic), memoryefficient, and robust with respect to changes in ambient light.

One possible method would be to translate an image by 0 through k numberof pixels in every possible direction with respect to the control image.For each of the (2*k+1)*(2k+1) translations, make a subtraction imageand calculate some metric to estimate the closeness to the controlimage. An example of such a metric would be the variance of thesubtraction image. The drawback of this method is that it is notefficient since for each of (2*k+1)*(2k+1) subtraction images, we wouldneed to calculate the variance over 512*512 pixels.

An efficient improvement of this algorithm is to estimate the varianceof the subtraction images by randomly selecting some small number ofareas of interest (for example, 9 areas of interest), each areaconsisting of a small number of pixels (say 8×8) from the image that onewishes to translate with respect to the control image. Also, choose somesearch depth (for example, 10 pixels) over which to translate thesesmall areas of interest with respect to their corresponding areas ofinterest in the control image. After translation in all possibledirections for 0 through 10 pixels, choose the translation whichminimizes the variance over the selected areas of interest. Since allthe areas of interest are the same size, division is not necessary inthe calculation of the variance which is to be ordered so that theminimal variance can be selected. Hence, all calculations can be carriedout in integer arithmetic. Since the areas of interest are sufficientlysmall, most of the data can be read into the host computer's RAMlimiting IO to the frame buffers and increasing speed.

Another problem is guaranteeing uniformity in the illumination of thetissue surface. Nonuniformity comes from fluctuation in the illuminationsource and intensity variations resulting from the three-dimensionalnature of the tissue surface. Fluctuation in the illumination source isaddressed by using a light feedback mechanism to regulate the powersupply of the illumination source. Both of these problems can also becompensated for in the image processing module.

The analog video signal is continuously fed into a means for processingthe signal. One such means for acquiring and analyzing data is an imageanalyzer (e.g., Series 151 Image Processor, Imaging Technologies, Inc.Woburn Mass.). An image analyzer can receive and digitize an analogvideo signal with an analog to digital interface and perform such afunction at a frame speed of about 1/30th of a second (e.g., 30 Hz or"video speed"). Processing the signal involves first digitizing thesignal into a series of pixels or small squares assigned a value (in abinary system) dependent upon the number of photons (i.e., quantity ofemr) being reflected off tissue from the part of the area of interestassigned to that pixel. For example, in a standard 512×512 image from acurrent technology CCD, there would be 262,144 pixels per image. In an 8bit system, each pixel is represented by 8 bits. One can cool the CCD toreduce thermal noise.

Preferably, the signal processing means includes a programmable look-uptable (e.g., CM150-LUT16, Imaging Technology, Woburn, Mass.) initializedwith values for converting gray coded pixel values, representative of ablack and white image, to color coded values based upon the intensity ofeach gray coded value. This provides image enhancement via an imagestretch. An image stretch is a technique whereby the highest and lowestpixel intensity values used to represent each of the pixels in a digitalimage frame are determined over a region of the image frame which is tobe stretched. Stretching a selected region over a larger range of valuespermits, for example, easier identification and removal of relativelyhigh, spurious values due to noise (e.g., glare).

Each image received is stored in the frame buffer, preferably within thecontext of a CPU as a frame of data elements represented, for example,as a 512 by 512 array of pixels. Each pixel has a 8 bit valuecorresponding to one of 256 levels of gray.

The processing means further includes a plurality of frame buffershaving frame storage areas for storing frames of digitized image datareceived from the A/D interface. The frame storage area comprises atleast one megabyte of memory space, and preferably at least 8 megabytesof storage space. An additional 16-bit frame storage area is preferredas an accumulator for storing processed image frames having pixelintensities represented by more than 8-bits. The frame buffers aretemporary fast memory. The processing means should include at leastthree frame buffers. One is for storing the averaged control image,another is for storing the subsequent image and a third is for storing adifference image between the averaged control image and the subsequentimage.

The processing means further includes an arithmetic logic unit (ALU)(e.g., ALU-150 Pipeline Processor) for performing arithmetical (add,subtract, etc.) and logical (and, or, etc.) functions from data locatedin one or more frame buffers. An ALU is a fast processor. The ALU allowsfor image averaging in real time. For example, a newly incomingdigitized image can be sent directly to the ALU and is added orsubtracted to an averaged control image sitting in a frame buffer bypassing both images through an ALU and adding them. After a last imageis added, this 16 bit result can be sent again through an ALU which willdivide this result by a constant (i.e., the total number of images). Theoutput from the ALU is either stored in a frame buffer, sent for moreprocessing, or used as its own input and again combined with anotherimage.

It is important to compensate for patient movement in the digitizedimages before subtracting such images. Thus, geometric transformationsare applied to the images so that they are geometrically registeredprior to subtraction.

The inventive apparatus can enhance processing speed to create adifference frame by adding a real time modular processor or faster CPUchip to the image processor. For example, one real time modularprocessor is a 150 RTMP-150 Real Time Modular Processor (ImagingTechnology, Woburn, Mass.).

The processing means further may include a means for performing ahistogram stretch of the difference frames (e.g., Histogram/FeatureExtractor HF 151-1-V module, Imaging Technology, Woburn Mass.) toenhance each difference image across its dynamic range. A linearhistogram stretch is described in, for example, Green, Digital ImageProcessing: A Systems Approach, Van Nostrand Reinhold, N.Y., 1983. Ahistogram stretch assigns the brightest pixel, or one with the highestvalue in the difference image and assigns this the maximum value. Thesmallest pixel value is assigned the minimum value and every other valuein between is assigned a linear value (for a linear histogram stretch ora logarithmic value for a log histogram stretch, etc.) in between themaximum and minimum values. This allows the difference image to fullyutilize the full dynamic range which provide for absolute changes.

The image processing system can use a variety of hardware that isavailable or under development. For example, the Texas InstrumentMultimedia Video Processor (MVP) is under development for motion videoapplications. The MVP uses a highly parallel internal architecture,large on-chip memory, and extremely high bandwidth communication withinCPU and between the CPU memory and I/O devices in order to provide inexcess of 2 billion RISC-type operations per second performancenecessary to support the requirement of real-time video compressionstandards and real-time image capture, processing and visualization. Forexample, the hardware can comprise of printed circuit board modules withinterfaces to a VME bus. A single chassis can house all of the modulesand reside on a rack that is easily transportable in an operating roomor between operating rooms, along with display monitors and peripheralinput and output devices. The real time system, for example, comprisesfour boards for acquisition image processing, peripheral control andhost computer. A minimal configuration with reducing processingcapabilities comprises just the acquisition and host computer boards.The acquisition board comprises circuitry to perform real-time averagingof incoming video frames and allow readout of averaged frames at amaximum rate bus. A VME bus is preferred because of its high peakbandwidth (greater than 80 Mbytes/sec for the latest revision, VME64)and compatibility with a multitude of existing VME products. Theacquisition board must also support many different types of cameras viaa variable scan interface. A daughter board can support the interfacingneeds of many different types of cameras and supply variable scansignals to the acquisition motherboard. Preferably, the unit comprises adaughter board interfacing to an RS-170A video signal to support a widebase of cameras. Other camera types, such as slow scan cameras with ahigher spatial/contrast resolution and/or better signal to noise ratio)can be developed and incorporated into the inventive device, as well asimproved daughter boards to accommodate such improved cameras.

The host computer comprises a single-board embedded computer with a VMEinterface. Preferably the host computer comprises a VME64 interface, ora standard (IEEE 1014-1987) VME interface, depending upon bus bandwidthconsiderations. Example of host computer boards include, for example,Force SPARC/CPU-2E and HP9000 Model 7471. The user interface can be, forexample, a Unix/X-Widow environment. The image processing board can be,for example, based upon Texas Instruments' MVP and other chips toperform real time image averaging, registration and other processingnecessary to produce high quality difference images for intraoperativeviewing. This board will also drive a 120×1024 RGB display to show asequence of difference images over time with pseudo-color mapping tohighlight tumor tissue. Preferably, a second monitor is used for thehost computer to increase the overall screen real estate and smooth theuser interface. The processing board (fully programmable) can support aVME64 master interface to control data transactions with the otherboards. Lastly, a peripheral control board can provide electricalinterfaces to control mechanical interfaces from the host computer. Suchmechanical interfaces can include, for example, a computer-controlled,motor-driven syringe for dye injection, light source, and camera controlbox.

The difference image signal is, preferably, further processed to smoothout the image and remove high frequency noise. For example, a lowpassspatial filter can block high spatial frequencies and/or low spatialfrequencies to remove high frequency noises at either end of the dynamicrange. This provides a smoothed-out processed difference image (indigital format). The digitally processed difference image can becolor-coded by assigning a spectrum of colors to differing shades ofgray. This image is then converted back to an analog image (by an ADIboard) and displayed for a real time visualization of differencesbetween an averaged control image and subsequent images. Moreover, theprocessed difference image can be superimposed over the analog image todisplay regions upon a video display of the area of interest, thosespecific tissue sites where the dye may have a faster uptake or where anintrinsic signal may be occurring.

The present invention further includes a means for subtractiveprocessing of difference images to identify cortical areas of neuronalinhibition. Normally areas of increased neuronal activity result in anincrease of the emr absorption capacity of neuronal tissue (i.e., thetissue gets darker if visible light is used for emr illumination, or anintrinsic signal increases in a positive direction). Similarly, adecrease in neuronal activity results in a decrease of emr absorptioncapacity of the tissue (i.e., the tissue appears brighter, or intrinsicsignals become negative). For example, image A is a subsequent averagedimage and image B is an averaged control image. Normally, when a pixelin image A is subtracted from a pixel in image B and a negative valueresults, this value is treated as zero. Hence, difference images cannotaccount for areas of inhibition. However, the present invention providesa method for identifying both negative and positive intrinsic signals,by the method comprising: (a) subtracting image A (a subsequent averagedimage) from image B (an averaged control image) to create a firstdifference image, whereby all negative pixel values are zero; and (b)subtracting image B from image A to create a second difference imagewhereby all negative pixel values are zero; and adding the first andsecond difference images to create a "sum difference image". The sumdifference image shows areas of increased activity (i.e., color codedwith warmer colors such as yellow, orange, red) and show areas of lessactivity or inhibition (i.e., color coded with colder colors such asgreen, blue, purple). Alternatively, one can overlay the firstdifference image on the second difference image. Either method providesan image of increased neuronal activity and decreased neuronal activity.

Preferably, the processing means further includes an optical disk forstoring digital image data, a printer for providing a hard copy of thedigital and/or analog video image and a monitor to provide for thephysician to continuously monitor the difference frame output (convertedback to an analog signal) of the apparatus. The difference frame outputmay be superimposed upon the real time analog video image to provide avideo image of the area of interest (e.g., cortical surface or suspectedtumor site) superimposed with a color-coded difference frame, in frozentime, to indicate where regions of faster dye uptake have occurred andwhere there are intrinsic signals in response to some stimulus orparadigm.

During a surgical procedure, there is often patient movement. In thecase of an anesthetized patient, motion is often due to respiration andblood flow. In an awake patient, there will be additional movement.Movement must be compensated for in the digitized images so that theimages are geometrically registered prior to subtraction. Geometriccompensation is achieved by applying geometric transformations to thedigitized images. One piece of image-processing hardware which canaccomplish geometric transformations in real-time is a GP-150Geometrical Processor board (Informatique et Techniques Avancees,Issy-les-Moulineaux, France). The GP-150 Processor board is compatiblewith Itex hardware and performs real time rotations, translations,zooms, and second degree distortion corrections at video rates withbilinear interpolation on 512×512×8-bit images.

Imaging Methods

The method for imaging a solid tumor involves periodically administeringa dye by bolus injection into vasculature (e.g., artery or vein)perfusing the suspected tumor site in the area of interest. Preferably,the dye has a relatively short half life (e.g., less than five minutes)and is rapidly cleared to allow for repeated administration. The videoCCD of the inventive apparatus is focused upon the suspected solid tumorsite (area of interest) and high intensity emr containing the wavelengthabsorbed by the dye illuminates the site. Just prior to administrationof the dye, the first averaged image is taken, digitized and stored in aframe buffer. The dye is injected quickly and rapidly as a bolus.Subsequent image frames are taken and stored and subtractively comparedto produce difference images (e.g., one or two per second) using theinventive processing means. Initial visualization of the dye will appearin the difference image first in tumor tissue because the dye perfusesmore rapidly into tumor tissue. Solid tumor margins will be the firstimages to appear in the difference frame as darkened lines outlining asolid tumor mass. This difference frame can be frozen and stored toallow the surgeon to study the tumor image and identify tumor margins inreal time during an operation. Moreover, the dye will remain for alonger period of time in tumor tissue compared to normal tissue.Therefore, after there is general appearance of the dye throughout thearea of interest in both normal tissue and tumor tissue, the dyeclearance in tumor tissue will be delayed, allowing another opportunityto visualize tumor margins by dye presence in tumor tissue but not innormal tissue. In more aggressive or malignant tumors, the higher thetumor grade, the longer the dye remains in tumor tissue. For lower gradeor more benign tumors, the dye remains in tumor tissue for 45 sec to 2min, whereas the dye can remain in more malignant tumors for up to 10minutes.

The inventive method is superior to established tumor imagingtechniques, such as MRI (magnetic resonance imaging) because opticalimaging can distinguish low grade tumors that cannot be distinguishedwith current MRI techniques (MRI is not an intraoperative technique) andupdated images are continually available during a surgical procedure byreadministering the dye. The dye can be administered on multipleoccasions during a surgical procedure after resection has begun to lookat resected walls for residual tumor tissue. For CNS tumors, MRItechniques require an advanced stage tumor that has compromised theblood brain barrier to be able to image such a tumor. The presentoptical imaging method, by contrast, can image even low grade tumorsthat have not yet compromised the blood brain barrier. Therefore,optical imaging is a more sensitive technique than MRI, can be usedintraoperatively, and provides an approximate means for grading tumors.

The dye can be any emr-absorbing dye that is safe for in vivoadministration, and has a short half-life when administeredintravenously or intraarterially. Example of such a dyes are indocyaninegreen, Photofrin®, NPe₆, BPD, Evans Blue, and combinations thereof.Further, during surgical resection of a solid tumor, it is importantthat the dye be rapidly cleared from the area of interest. In that way,there can be repeated dye administrations to determine residual tumortissue during resection.

During an imaging study, it is important to continually update theaveraged image frame to account for patient movement, particularly foran awake patient. This will account for circulating residual dye andpatient movements or tissue movements due to surgical manipulation.

For example, the inventive method for visualizing tumor tissue wasutilized in a rat glioma model of the frontal lobe to test the abilityof the inventive method to identify normal or edematous brain from tumortissue and to determine if the inventive method could separate normalfrom abnormal tissue after all visible tumor had been resected. Thedynamic nature of the dye's perfusion through the brain's vasculatureallowed for differentiation of normal brain and tumor tissue. Moreover,with a spatial resolution of optical images below 20 μm² /pixel, evensmall areas of residual tumor can be identified. Further, tumor tissuewas identified through intact rat skull, providing a method and a devicefor tumor imaging prior to surgery.

Without being bound by theory, the dynamic differences in dye perfusionthrough normal brain tissue surrounding and tumor tissue could beaccounted for by any one or a combination of the following four reasons:(1) greater extravasation of the dye through leaky tumor capillaries;(2) more rapid uptake of the dye by tumor tissue; (3) slower transittimes through tumor tissue; and (4) preferential uptake of the dye bytumor cells.

The rat glioma model has been examined and microvasculature compared tonormal cortex. Blood flow in tumor tissue is slower and more variablethan in normal tissue. The difference between tumor and normal brainhave been attributed to tumor location, degree of infiltration, andnecrosis. In other studies using cultured spheroids of C6 astroglialcells transplanted into rat brain, blood flow was slower in viable tumorthan in normal rat brain. Microvessel volume fraction was equivalentbetween tumor and normal brain. However, since only about 50% of thetumor was actively perfused, the surface area of perfused microvesselsin the tumor was one-half that of the normal brain. These changes couldaccount for a slower flow of dye through the tumor compared to normalbrain and also lead to more rapid clearance by the normal brain incontrast to the tumor.

The permeability of tumor capillaries is much higher than in normalbrain. Leakiness of these capillaries leads to extravasation of largerparticles resulting in edema and an increase in interstitial pressuresurrounding tumor microvessels. Since tumor microvessels do not containnormal arteriole smooth muscle, they also have no local control ofpressure gradients. This leads to a stasis of flow in tumor tissue. Theoverall effect on dye perfusion is longer transit times than in normalbrain and an increase which prolongs duration of the large opticalsignal from tumor tissue. Such reasoning supports the dynamic changes inoptical signal from tumor and normal brain that was seen during dyeperfusion. There is nearly equivalent uptake, but a much slower transittime in tumor tissue resulting in prolonged increases in optical signal.Also, tissue surrounding the tumor is expect to have increases ininterstitial pressures but without leaky capillaries and othermicrovasculature changes, accounting for the fact that tumor tissue hadan intermediate duration of optical changes.

It is not clear whether a more rapid clearance mechanism of the dye fromnormal brain was occurring or if the dye was being preferentiallysequestered by tumor cells. In the later case, hematoporphyrins arepreferentially taken up into tumor cells, and accounts for the abilityto use photodynamic therapy on such cells. If the dye remainedcompletely intravascular, then a very uneven distribution of dye betweennormal and tumor cells would be expected. However, we observed theopposite from many intermediate images taken from areas between largerpial microvessels.

The present invention further provides a method for imaging of corticalfunctional areas and dysfunctional areas, such as those areas of severeepileptic activity. The method involves administering a sensory signalfor mapping a particular cortical function, or identifying an area ofhyperactivity that is the location of epileptic activity in an epilepticpatient. An epileptigenic area of the cortex will be visualized asspontaneously more active and can be imaged by the inventive apparatususing a method for mapping intrinsic signals of cortical activity.

The method for visualizing intrinsic signals involves stimulatingcortical tissue with specific paradigms. Various paradigms include, forexample, presenting pictures of objects to a patient and asking thepatient to name the object to alter neuronal activity which will resultin an associated intrinsic signal.

Another feature of the inventive apparatus and method is the ability toimage peripheral nerve damage and scarring. Nerves of the central andperipheral nervous system (PNS) are characterized by the ability toregenerate after damage. During operations to repair damaged peripheralor cranial nerves, one can image areas of nerve damage by imaging areasof blockage of intrinsic signals. For example, the nerve is exposed inthe area of interest. The nerve is stimulated upstream of the site ofdamage. The active nerve pathway is imaged by intrinsic signals in theprocessed difference frame after activation. The site of nerve damage orblockage is evidenced by an abrupt end or diminution to the intrinsicsignal at the damage site. In this way, the surgeon is able to obtainreal time information precisely where there is nerve damage and tocorrect the damage, if possible.

Moreover, the inventive apparatus and ability to image intrinsic signalscan be used when there is a need to remove tumor tissue that is locatedsurrounding or adjacent to nerve tissue. For example a tumor called anacoustic neuroma is usually located surrounding an auditory (hearing)nerve. It is often a difficult procedure to remove tumor tissue withoutsevering the auditory nerve (a cranial nerve) and causing one ear tobecome deaf or damage the facial nerve that innervates muscles that movethe face. The inventive methods provide an ability to distinguish tumortissue from surrounding nerve tissue using a dye. Additionally, theinventive method can continually provide information to the surgeonshowing the precise location of the auditory or facial nerve bycontinually or periodically stimulating the nerve with a sound paradigmfor the auditory nerve, or backfiring the facial nerve from a facialmuscle, and detecting the intrinsic signal associated with nerveactivity. Accordingly, when there is tumor tissue in close proximity tonerve tissue, one can use both the ability to locate tumor tissue with adye and to locate nerve tissue by detecting intrinsic signal using thesame imaging apparatus.

The imaging method can obtain information at the surface of an area ofinterest or can target an area of interest at a level deeper in tissue.Longer wavelengths of emr used to form the image (averaged control imageand subsequent averaged images) can be used to probe areas of interestwhich are deeper into tissue. Moreover, if a difference image is createdbetween the image seen with 500 nm emr and the image seen with 700 nmemr, the difference image will show an optical slice of tissue.Moreover, instead of using cutoff filters, administration of a dye canact as a tissue filter of emr to provide a filter in the area ofinterest. In this instance, it is desirable to utilize a dye thatremains with tumor or normal tissue for a prolonged period of time.

The present invention further comprises a method for enhancingsensitivity and contrast of the images obtained from tumor tissue orintrinsic signal difference images, comprising: (a) illuminating an areaof interest with a plurality of wavelengths of emr, wherein there is atleast a first wavelength of emr and a second wavelength of emr; (b)obtaining a series of frames corresponding to each wavelength of emr,wherein a first sequence of frames is from the first wavelength of emr,the second sequence of frames is from the second wavelength of emr andso on; (c) processing the first sequence of frames into a first averagedcontrol image, the second sequence of frames into a second averagedcontrol image and so on; (d) stimulating for intrinsic signals oradministering a dye for tumor tissue imaging; (e) obtaining a firstseries of subsequent frames using the first wavelength of emr, a secondseries of subsequent frames using the second wavelength of emr, and soon, and processing the first, second and so on subsequent series offrames into the first, second and so on subsequent averaged images,respectively; (f) obtaining a first difference image by subtracting thefirst averaged control image from the first subsequent averaged imageand a second difference image by subtracting the second averaged controlimage from the second subsequent averaged image, and so on; and (g)obtaining an enhanced difference image by ratioing the first differenceimage to the second difference image. This can be accomplished, forexample, with two single wavelength sources of emr, or by using a broadmultiple wavelength source of emr and a plurality of longpass filters.Preferably, the monochromatic emr to illuminate the area of interest arefrom laser sources.

The inventive apparatus and methods for imaging intrinsic signals andtumor tissue can operate outside of a surgical procedure setting. Morespecifically, it is possible to obtain tissue imaging through intactskin and bone. In some areas of the body longer wavelength visible lightand near infrared emr can easily pass through such tissue for imaging,such as breast tissue. With dye injection, areas of increasedvascularity, such as tumor tissue can be identified.

Yet another aspect of the inventive method involves using an emrabsorbing or fluorescent dye conjugated to a targeting molecule, such asan antibody, or more particularly, a monoclonal antibody or fragmentthereof specific for an antigen surface marker of a tumor cell. The areaof interest is illuminated with emr containing excitation wavelengths ofthe fluorescent dye but not emission wavelengths. This can beaccomplished by use of a cutoff filter over the emr source. Preferably,the CCD camera is coupled to an image intensifier or micro channel plate(e.g., KS-1381 Video Scope International, Wash D.C.) to increase thesensitivity of the system by several orders of magnitude and allow forvisualization of cells having fluorescent dyes attached hereto. Examplesof fluorescent dyes that can be conjugated to a targeting moleculeinclude, for example, Cascade Blue, Tex. Red and Lucifer Yellow CH fromMolecular Probes Eugene OR.

Still further applications of the inventive device are possible. Forexample, the device can be used for calibration of electrodes used toelectrically stimulate the cortical surface (see, for example, FIGS. 1and 2). A technique presently used by surgeons to map the functionalorganization of an awake patients cortex is to directly apply current(via stimulating-electrodes) to the surface of the cortex. The surgeonwould like to apply the greatest intensity of stimulating current aspossible without triggering an epileptic seizure or causing tissuedamage. As a method of calibrating the stimulating-electrodes, thesurgeon stimulates the cortex of the patient with currents of varyingintensities and monitors the electrical activity by observing the outputof recording-electrodes which have been placed directly on the surfaceof the patients brain. The surgeon applies current for several secondswith the stimulating-electrodes and checks the output from therecording-electrodes for afterdischarge epileptiform activity which maypersist for a period of time after stimulation has ceased. The inventivedevice provides an accurate means of monitoring spatial extent of thecortex affected by the electrode stimulation current, and time course(if any) of stimulation-evoked activity persisting after cessation ofthe stimulation current. The method comprises acquiring a control imageprior to stimulation, and then acquiring subsequent images during andafter stimulation. The images are processed as described herein toprovide a highly resolved spatial map of those areas of the cortex whoseresting activity has been affected by the applied stimulating current.The inventive device provides a map of the spatial extent and timecourseof stimulation and epileptic activity, which the surgeon can use tochoose an appropriate stimulating current for his or her electrodes.

It is also possible to utilize the inventive device for simultaneousspatial mapping of dynamic changes of blood-flow in blood vessels andcortical activity using intrinsic optical changes. (see, for example,FIGS. 1 and 2) Without being bound by theory, optical changes withinregions of larger blood vessels are due to a rate of change of theflow-rate within these vessels. The invention provides a method formonitoring these changes of flow within individual blood vessels.

FIG. 20 illustrates a view of hind limb somatosensory cortex in ananesthetized rat to demonstrate measurement of blood flow rates withinvessels of diameters as small as 2 micrometers in accordance with thepresent invention, thereby providing spatial resolution that is fargreater than conventionally available. FIG. 20A shows a gray-scale imagemapping an area of a rat cortex that is approximately 1 mm by 1 mmshowing exemplary data acquisition boxes 1, 2, and 3 encompassing anarterial, a venule, and cortical tissue, respectively. The image mappingof FIG. 20 is formed with a CCD camera (COHU 6500) that is fitted to anoperating microscope and acquires image frames are 512×480 pixels at 30Hz. The image frames are preferably digitized at 8 bits using a Series151 system from Imaging Technology Inc. of Woburn, Mass. The 2 micronimage resolution represents the resolution of individual pixels withinthe 1 mm by 1 mm mapping, which allows individual capillaries to bedistinguished. It will be appreciated that higher spatial resolutionscan be achieved with even greater microscopic magnification.

FIG. 20B shows plots of percentage change of emr absorption per secondin the spatial regions of boxes 1, 2, and 3 and a plot of correspondingmorphological measurements of the venule in the spatial region of box 2.The change in emr absorption is measured during activation ofsomatosensory cortex in an anesthetized rat by direct stimulation of thesciatic nerve in the hind limb of the rat relative to a baseline levelof somatosensory cortical activity prior to stimulation. Each data pointcorresponds to an average of pixel values within the correspondingsample box shown in FIG. 20A obtained from 16 frames over about 1/2second at intervals of one second.

Differences in blood flow rate correspond to differences in emrabsorption and, therefore, differences in the light received by the CCDcamera. For example, increased flow of oxygenated blood corresponds toan increase in the ratio of oxyhemoglobin to deoxyhemoglobin, whichwould appear brighter (or darker) if the emr detected by the CCD camerais filtered to pass red (or green) light. Similarly, increased flow ofdeoxygenated blood corresponds to a decrease in the ratio ofoxyhemoglobin to deoxyhemoglobin, which would appear darker (orbrighter) if the emr detected by the CCD camera is filtered to pass red(or green) light. Moreover, the ability to measure blood flow changesover periods of 0.5 second or less provides a temporal resolution forblood flow measurement in small vessels that contrasts very favorablywith conventional techniques that are capable of detecting blood flowchanges only over periods of several minutes or more.

FIG. 20B shows positive-going changes in emr absorption corresponding toincreased flow of oxygenated blood in the arterial encompassed by box 1in FIG. 20A. The plot represents a period that includes a baseline levelof cortical activity prior to stimulation of the sciatic nerve,stimulation of the nerve, and a subsequent recovery period. FIG. 20Balso shows corresponding negative-going changes in emr absorptioncorresponding to increased flow of deoxygenated blood in the venuleencompassed by box 2 in FIG. 20A. These plots demonstrate theeffectiveness of measuring positive- and negative-going emr absorptionrepresenting blood flow at high spatial and temporal resolutions inaccordance with the present invention.

FIG. 20B also shows corresponding morphological measurements of thediameter of the venule in the spatial region of box 2. The morphologicalmeasurements correspond to widths of the venule measured from videoimages. As is known in the art, vessel diameter relates to blood ratesby a power of three. This plot serves as a control of the plotted bloodflow rates measured in accordance with the present invention. It will beappreciated, however, that the blood flow rates measured in accordancewith the present invention have significantly higher resolution than therelatively simple morphological measurements.

FIG. 20B further shows changes in emr absorption in the somatosensorycortical tissue encompassed by box 3 in FIG. 20A. These emr absorptionchanges may relate to the plotted blood flow changes, as well as otherintrinsic tissue characteristics. This demonstrates how the high spatialand temporal resolutions with which emr absorption can be measured inaccordance with the present invention can allow determination of whetherchanges in tissue characteristics correlate to blood flow rates or otherintrinsic factors.

FIG. 20C is a sequence of pseudocolor images showing dynamic changes ofoptical signals corresponding to blood flows plotted in FIG. 20B. FIG.20C1 represents a control image corresponding to baseline corticalactivity prior to stimulation of the rat sciatic nerve. FIGS. 20C2 and20C3 represent successive difference images corresponding topositive-going changes in emr absorption following stimulation of therat sciatic nerve. FIGS. 20C4, 20C5, and 20C6 represent successivedifference images corresponding to positive-going changes in emrabsorption of cortical tissue during the recovery following stimulationof the rat sciatic nerve. In these Figures, stimulation causes arterialsto show increased red brightness, which corresponds to increased flow ofoxygenated blood. Venules appear darker in response to stimulation,corresponding to increased flow of deoxygenated blood. Figures duringthe recovery period show blood flow rates returning to baseline amounts.

FIG. 20D is a pair of pseudocolor images formed by converse subtractivecalculations to show the opposite changes of optical signalscorresponding to arterials and venules. FIG. 20D1 is analogous to FIGS.20C in that it is a difference image selected to show with increased redbrightness arterials with increased flow of oxygenated blood. FIG. 20D2is a difference image that is the converse of the one in FIG. 20D1 toshow with increased red brightness venules with increased flow ofdeoxygenated blood. FIG. 20D shows that converse difference images,which can be rendered individually (as shown) or together, can be usedto illustrate opposing emr absorption changes relating toarterial/venule or oxygenated/deoxygenated blood flow.

In a preferred embodiment, blood flow changes are measured by a methodcomprising: (a) illuminating a tissue area of interest with highintensity emr, wherein the area of interest comprises one or morevessels of interest; (b) obtaining a first series of frames of the areaof interest over a period of, for example, about 0.5 second (i.e., 16frames), and processing the series of frames into an averaged controlimage; (c) obtaining a second series of subsequent frames of the area ofinterest over a period of, for example about 0.5 second (i.e., 16frames), and processing the series of subsequent frames into asubsequent averaged image; and (d) obtaining a first difference image bysubtracting the averaged control image from the subsequent averagedimage, or vice versa, and whereby all negative pixel values are zero, toidentify changes in blood flow rate in the vessel between the controland subsequent time periods. To obtain an image showing blood flowcharacteristics with emr absorption characteristics opposite thoseidentified in step (d), a second difference image is obtained byperforming a converse subtraction to that performed in step (d), wherebyall negative pixel values are zero. The first and second differenceimages may be used separately or added together to create a "sumdifference image"

It will be appreciated that the blood flow measurements with the highspatial and temporal resolution of the present invention have a numberof applications. It allows, for example, identification ofinterconnected tissue by clipping selected vessels to see where bloodflow is interrupted, checking of revascularization of grafted orattached tissue, or testing of stroke treatments or effectiveness orpassage of drugs.

It is also possible to utilize the inventive device for a method to mapfunctional organization of the cortex in an anesthetized patient duringa neurosurgical procedure. (see, for example, FIG. 5) A method comprisesproviding an afferent sensory stimulation to activate an area of cortexspecific to processing this afferent sensory input, and using the evokedintrinsic signals observed with the inventive device and method tooptically map regions of the cortex involved in the afferent sensorystimulation.

For example, during surgical removal of a tumor, the surgeon may need toknow which area of cortex is involved in processing sensory input from alimb in an anesthetized patient. The surgeon can apply an afferentsensory input, such as a vibrating stimulus, to the limb to cause theconduction of information to that part of the cortex involved in sensoryprocessing for this limb. This sensory stimulus will activate theappropriate region of cortex which can then be mapped using theinventive device and method. Other examples of afferent sensory inputinclude providing sound stimuli to activate auditory cortex, visualstimuli to map visual cortex, moving a joint, etc. This method is usefulfor mapping certain functional areas in an anesthetized patient.

It is also possible to utilize the inventive device for a method to aidthe surgeon intraoperatively in the acquisition of biopsies from a tumormargin. (see, for example, FIG. 12). After the surgical removal of atumor, the surgeon is left with the task of trying to distinguish whatareas of the tumor resection margin contain remnants of tumor tissue.This is important since it is thought that in many tumor types, theeffectiveness of post-operative radiation and chemotherapy is correlatedto the amount of tumor tissue left behind. The present technique ofidentifying these remnants of tumor is for the surgeon to remove smallamounts of tissue in a random sampling fashion and send these samplesfor analysis to a pathologist. This sampling and analysis must occurduring the course of the surgery, since the surgeon needs thisinformation to base his or her surgical decisions as to what tissueshould be further resected. This technique suffers from severaldrawbacks, such as, the time required increases the length of thesurgery thus increasing the cost and risk to the patient, and the randomsampling method of determining biopsy sights is inevitably fraught withsampling error. The present invention provides a method for quicklydetermining the likely sites of remnant tumor tissue within a tumormargin. This can assist a surgical decision as to what areas of tissueshould be sampled for biopsy purposes and which areas should beimmediately removed a likely containing tumor tissue.

EXAMPLE 1

This example illustrates optical changes on a human subject by directcortical electrical stimulation. Surface electrical recordings (surfaceEEG, ECOG) were correlated with optical changes. FIGS. 1 and 2illustrate dynamic changes in intrinsic optical properties of humancortex during direct electrical stimulation of the cortex and duringepileptiform activity. Figures A, B, C, and D, exemplify a typicaloptical imaging study where the inventive device is utilized to providedynamic information with high spatial resolution of the intrinsicoptical changes of the human cortex during either awake or anesthetizedneurological surgical procedures. In FIG. 1, intrinsic optical changeswere evoked in an awake patient during stimulating-electrode"calibration". Four stimulation trials were sequentially applied to thecortical surface, each stimulation evoking an epileptiform afterdischageepisode. A stimulation trial consists of: 1) monitoring resting corticalactivity by observing the output of the recording electrodes for a briefperiod of time, 2) applying and electric current via thestimulation-electrodes to the cortical surface at a particular currentfor several seconds, and 3) monitoring the output of the recordingelectrodes for a period of time after stimulation has ceased.

A series of images (each image consisting of an average of 128 framesacquired at 30 Hz) were acquired during each of the four stimulationtrials. A current of 6 mA was used for the first three stimulationtrials, and 8 mA for the fourth. After a sequence of 3-6 averagedcontrol images were acquired, a bipolar cortical stimulation current wasapplied (either 6 mA or 8 mA) until epileptiform after dischargeactivity was evoked (as recorded by the surface electrode). Images werecontinuously acquired throughout each of the four stimulation trials.

The percentage change in absorption of light for each pixel wascalculated for each image acquired during the four stimulation trials.The average percentage changes over the four areas (indicated by thefour square regions marked in FIG. 1A) were plotted graphically in FIG.1B, 1C, and 1D for comparison and analysis of the dynamic changesoccurring in these four spatial areas.

FIG. 1A is a view of a human cortex just anterior to face-motor cortexwith one recording electrode (r) and two stimulating electrodes (s), andfour sites (the boxed areas labeled 1, 2, 3, and 4) where the averagepercent changes of absorption over these areas were determined. Thecortex was illuminated with emr >690 nm. The scale bar is 1 cm.

FIG. 1B are plots of the percent change of emr absorption per second inthe spatial regions of boxes 1 and 3 (as labeled in FIG. 1A). For bothregions, the peak change is during the fourth stimulation trial (at 8mA) in which the greatest amount of stimulating current had induced themost prolonged epileptiform afterdischarge activity. The changes withinbox 3 were greater and more prolonged than those of box 1. Box 3 wasoverlying the area of epileptic focus (the excitable area of tissuepossibly responsible for the patients epilepsy).

FIG. 1C show plots of the percent change of emr absorption per second inthe spatial regions of boxes 1 and 4 (as labeled in FIG. 1A). Box 1overlays and area of cortical tissue between the two stimulatingelectrodes, and box 4 overlays a blood vessel. The changes within box 4are much larger and in the opposite direction of box 1, and also thatthese changes are graded with the magnitude of stimulating current andafterdischarge activity. Since the changes in box 4 are most likely dueto changes of the blood-flow rate within a blood vessel, this plot showsthat the invention can simultaneously monitor cortical activity andblood-flow.

FIG. 1D shows plots of the percentage change of emr absorption persecond in the spatial regions of boxes 1 and 2 (as labeled in FIG. 1A).Note that although these two areas are nearby each other, their opticalchanges are in the opposite direction during the first three stimulationtrials using 6 mA current. The negative going changes within the regionof box 2 indicate that the invention may be used to monitor inhibitionof cortical activity as well as excitation.

All imaging procedures reported in this example and the patient consentform were reviewed and approved by the University of Washington HumanSubjects Review Committee. All patients signed an informed consentstatement for both the surgery and the imaging experiments. The cortexwas evenly illuminated by a fiberoptic emr passing through a beamsplitter, controlled by a D.C. regulated power supply (Lambda, Inc.) andpassed through a 695 nm longpass filter. Images were acquired with a CCDcamera (COHU 6500) fitted to the operating microscope with a speciallymodified cineadaptor. The cortex was stabilized with a glass footplate.Images were acquired at 30 Hz and digitized at 8 bits (512×480 pixels,using an Imaging Technology Inc. Series 151 system, Woburn Mass.).Geometrical transformations were applied to images to compensate forsmall amounts of patient motion (Wohlberg, Digital Imaging Warping,I.E.E.E. Computer Society, Los Alamitos, Calif., 1988). Subtraction ofimages collected during the stimulated state (e.g., during corticalsurface stimulation, tongue movement or naming) from those collectedduring a control state with subsequent division by a control imageresulted in percentage difference maps. Raw data (i.e., no digitalenhancement) were used for determining the average optical change inspecified regions (average sized boxes was 30×30 pixels or 150-250 μm²).For pseudocolor images, a linear low pass filter removed high frequencynoise and linear histogram transformations were applied. Noise wasdefined as the standard deviation of fluctuations in sequentiallyacquired control images as 0.003-0.009.

The optical changes between the stimulating electrodes (site #1 FIG. 1A)and near the recording electrode (site #3) showed a graded response tothe intensity and duration of each afterdischarge episode (FIG. 1B). Thespatial extent of the epileptiform activity was demonstrated bycomparing a baseline image collected before stimulation to thoseobtained immediately after stimulation. The intensity and spread of theoptical changes were much less following #2 (shortest least intenseafterdischarge episode) than after stimulation #4 (longest most intenseafterdischarge episode).

When the optical changes were below baseline, the surface EEG recordingsdid not identify epileptiform activity (n=3 patients) At site #3 in FIG.2A1, the optical changes after stimulation were below baseline (i.e.,black regions in FIG. 2A3). However, during the fourth stimulation, theepileptiform activity spread into the area of site #3 and the opticalsignal did not go below baseline until later (site #3, FIG. 1B). Thisnegative optical signal likely represents inhibited neuronal populations(an epileptic inhibitory surround), decreased oxygen delivery, or bloodvolume shunted to activated regions.

Identification of cortical areas of neuronal inhibition is shown withreference to FIG. 21, which illustrates a view of human cortex justanterior to face-motor cortex with one recording (r) and two stimulatingelectrodes (s). The cortex was illuminated with emr of wavelengthsgreater than about 690 nm and FIGS. 21B-21E represent changes inabsorption of emr over different periods. Regions colored red, blue, andblack correspond to increasing (positive-going), decreasing(negative-going), and non-changing levels of cortical (neuronal)activity, respectively. Normally areas of increased neuronal activity(or intrinsic signals) result in an increase of emr absorption capacityof neuronal tissue (i.e., the tissue appears darker if visible light isused for emr illumination). Similarly, a decrease in neuronal activity(or intrinsic signals) results in a decrease of emr absorption capacityof the tissue (i.e., the tissue appears brighter).

FIG. 21B is a spatial map of baseline cortical activity prior toapplication of stimulating current for inducing epileptiform activity.The baseline cortical activity corresponds to period A in the EEGrecording of surface electrical signals received by recording electrode(r) shown in FIG. 22.

FIG. 21C is a spatial map of cortical activity during 6 mA stimulationat stimulating electrodes (s) and the resulting epileptiformafterdischarge activity. This cortical activity corresponds to period Bin the EEG recording of surface electrical signals received by recordingelectrode (r) shown in FIG. 22. FIG. 21C shows a large red region thatencompasses recording electrode (r) and corresponds to increasing(positive-going) cortical (neuronal) activity and significantly elevatedsignal levels in the EEG recording. However, the elevated signal levelsover period B in the EEG recording mask large surrounding blue regioncorresponding to decreasing (negative-going) cortical (neuronal)activity.

FIG. 21D is a spatial map of cortical activity during an apparentquiescent period following the epileptiform afterdischarge activityinduced by stimulation at stimulating electrodes (s). This corticalactivity corresponds to period C in the EEG recording of surfaceelectrical signals received by recording electrode (r) shown in FIG. 22.The apparently quiescent nature of period C is based upon theconventional interpretation of the decreased signal levels in the EEGrecording over this period. FIG. 21D shows a major blue region thatencompasses recording electrode (r) and corresponds to decreasing(negative-going) cortical (neuronal) activity. However, the decreasedsignal levels over period C in the EEG recording mask a significant blueregion, extending between stimulating electrodes (s) but not torecording electrode (r), corresponding to increasing (positive-going)cortical (neuronal) activity. As a result, the decreased or quiescentsignal levels over period C in the EEG recording mask a significant redregion corresponding to increasing (positive-going) cortical (neuronal)activity.

FIG. 21E is a spatial map of cortical activity during a period followingthe quiescent period represented by FIG. 21D. This cortical activitycorresponds to period D in the EEG recording of surface electricalsignals received by recording electrode (r) shown in FIG. 22. FIG. 21Eshows a region of mixed red and blue subregions that encompassesrecording electrode (r) and corresponds to increasing (positive-going)cortical (neuronal) activity and signal levels in the EEG recording thatare elevated in comparison to the quiescent characteristics of period C.However, the elevated signal levels over period D in the EEG recordingmask large adjacent red region corresponding to increasing(positive-going) cortical (neuronal) activity.

Cortical areas of neuronal inhibition may be identified by subtractiveprocessing of difference images. For example, image A is a subsequentaveraged image and image B is an averaged control image (e.g., thespatial map of baseline cortical activity shown in FIG. 21B).Conventionally, when a pixel in image A is subtracted from a pixel inimage B and a negative value results, this value is treated as zero.Hence, difference images cannot account for areas of inhibition. This isa disadvantage of conventional EEG techniques, as well as conventionaloptical imaging, magnetic resonance imaging, and positron emissiontomography.

However, the present invention provides a method for identifying bothnegative and positive neuronal activity (intrinsic signals) by themethod comprising: (a) subtracting image A (a subsequent averaged image)from image B (an averaged control image) to create a first differenceimage, whereby all negative pixel values are zero; and (b) subtractingimage B from image A to create a second difference image whereby allnegative pixel values are zero; and adding the first and seconddifference images to create a "sum difference image". The sum differenceimage shows areas of increased activity (i.e., color coded with warmercolors such as yellow, orange, red) and show areas of less activity orinhibition (i.e., color coded with colder colors such as green, blue,purple). The spatial maps of FIGS. 21C, 21D, and 21E were generated inthis manner. Alternatively, one can overlay the first difference imageon the second difference image. Either method provides an image ofincreased neuronal activity and decreased neuronal activity.

The high resolution of the spatial maps in FIGS. 21C-21E, together withidentification of areas of both increased and decreased neuronalactivity, can be used by a neurosurgeon intraoperatively to identifyprecisely areas in the brain affected by epileptiform afterdischargeactivity. This allows neurosurgery to be performed with minimal damageto other cortical areas

FIG. 3 shows a sequence of dynamic changes of optical signalsidentifying active areas and seizure foci. This Figure shows eightpercentage difference images from the stimulation trial 2 described inFIGS. 1 and 2. Each image captures a two second interval. Note the focalarea of greatest optical change in the center of images 4, 5, and 6 thatindicates the region of greatest cortical activity. The magnitude ofoptical change is indicated by the gray-scale bar in FIG. 2. Each imagemaps an area of cortex approximately 4 cm by 4 cm.

FIG. 4 illustrates a real-time sequence of dynamic changes ofstimulation-evoked optical changes in human cortex. FIG. 4, panels 1through 8, show eight consecutive percentage difference images, eachimage is an average of 8 frames (<1/4 second per image). Each image mapsto an area of cortex that is approximately 4 cm by 4 cm.

In FIG. 6, stimulation mapping of the cortical surface was performed onawake patients under local anesthesia to identify sensory/motor cortexand Broca's areas. During three "tongue wiggling" trials images wereaveraged (32 frames, 1 sec) and stored every 2 second. A tongue wigglingtrial consisted of acquiring 5-6 images during rest, then acquiringimages during the 40 seconds that the patient was required to wiggle histongue against the roof of his mouth, and then to continue acquiringimages during a recovery period. The same patient was then required toengage in a "language naming" trial. A language naming trial consistedof acquiring 5-8 images during rest (control images--the patientsilently viewing a series of blank slides), then acquiring images duringthe period of time that the patient engaged in the naming paradigm(naming a series of objects presented with a slide projector every 2seconds, selected to evoke a large response in Broca's area), andfinally a series of images during the recovery period following the timewhen the patient ceased his naming task (again viewing blank slideswhile remaining silent). Images A1 and B1 are gray-scale images of anarea of human cortex with left being anterior, right-posterior,top-superior, and the Sylvan fissure on the bottom. The two asterisks onA1, B1, A2, and B2 serve as reference points between these images. Thescale bars in the lower right corner of A1 and B1 are equal to 1 cm. InA1, the numbered boxes represent sites where cortical stimulation withelectrical stimulating electrodes evoked palate tingling (1), tonguetingling (2), speech arrest-Broca's areas (3,4) and no response(11,12,17,5-premotor). Image A2 is a percentage difference control imageof the cortex during rest in one of the tongue wiggling trials. Thegray-scale bar on the right of A2 shows the relative magnitude of thegray scale associated with images A2, A3, B2 and B3. Image A3 is apercentage difference map of the peak optical changes occurring duringone of the tongue wiggling trials. Note that areas identified as tongueand palate sensory areas by cortical stimulation show a large positivechange. The suppression of baseline noise in surrounding areas indicatethat, during the tongue wiggling trials, language-motor areas showed anegative going signal. Image B2 is a percentage difference control imageof the cortex during one of the language naming trials. Image B3 is apercentage difference image of the peak optical change in the cortexduring the language naming task. Large positive going signals arepresent in Broca's area, however negative going signals are present intongue and palate sensory areas.

FIG. 23 shows functional mapping of human language (Broca's area) andtongue and palate sensory areas in an awake human patient. FIGS. 23A1and 23B1 are gray-scale images of an area of human cortex with leftbeing anterior, right-posterior, top-superior, and the Sylvan fissure onthe bottom. The numeral 34 in FIG. 23A1 (partly obscured) serves asreference point to FIG. 23B1 in which the numeral is mostly obscured atthe upper right edge of the Figure. FIG. 23A2 and 23B2 are spatial mapsof cortical activity in the areas of human cortex shown in FIGS. 23A1and 23B1 during, respectively, a language naming exercise and a tonguewiggling exercise.

FIGS. 23A2 and 23B2 each corresponds to an average of approximately 32frames which were acquired at 30 Hz over a period of about 1 second andstored about every 2 seconds. The cortex was illuminated with emr ofwavelengths greater than about 690 nm and FIGS. 23A2 and 23B2 representchanges in absorption of the emr over different periods. Regions coloredred, blue, and black correspond to increasing (positive-going),decreasing (negative-going), and non-changing levels of corticalactivity, respectively.

FIG. 23A2 is a spatial map of cortical activity during a language namingexercise and shows on the right side a major red region that correspondsto increasing (positive-going) cortical (neuronal) activity on the leftside a major blue region that corresponds to decreasing (negative-going)cortical (neuronal) activity. FIG. 23B2 is a spatial map of corticalactivity during a tongue wiggling exercise and shows on the left side amajor red region that corresponds to increasing (positive-going)cortical (neuronal) activity on the right side a major blue region thatcorresponds to decreasing (negative-going) cortical (neuronal) activity.FIGS. 23A2 and 23B2 show related inhibition and excitation of corticalactivity for different functional activities. This allows improvedmapping and understanding of cortex operation, both intraoperatively andphysiologically. For example, the high resolution of the spatial maps inFIGS. 23A2 and 23B2, together with identification of areas of bothincreased and decreased neuronal activity, can be used by a neurosurgeonintraoperatively to identify precisely areas in the brain dedicated toimportant functions such as vision, movement, sensation, memory andlanguage. This allows neurosurgery to be performed with minimal damageto cortex areas dedicated to important functions.

FIG. 7 shows a time course and magnitude plot of the dynamic opticalchanges in human cortex evoked in tongue and palate sensory areas and inBroca's area (language). FIG. 7 shows the plots of the percentage changein the optical absorption of the tissue within the boxed regions shownin FIG. 6, images A1 and B2, during each of the three tongue wigglingtrials and one of the language naming trials. Diagram 7A shows the plotsduring the three tongue wiggling trials averaged spatially within theboxes 1, 2, 3, and 4 as identified in FIG. 6, image A1. Diagram 7B showsthe plots during one of the language naming trials average spatiallywithin the boxes 1-7 and 17. These results agree with those datareported by Lee et al. (Ann. Neurol. 20:32, 1986) who reported largeelectrical potentials in the sensory cortex during finger movement. Themagnitude of the optical changes in the sensory cortex during tonguemovement (10-30%) parallels sensory/motor cortex studies where cerebralblood flow increases 10-30% during motor tasks (Colebatch et al., J.Neurophysiol. 65:1392, 1991). Further, utilizing Magnetic ResonanceImaging (MRI) of blood volume changes in human visual cortex duringvisual stimulation, investigators have demonstrated increases of up to30% in cerebral blood volume (Belliveau et al., Science 254:716, 1991).Further, utilizing Magnetic Resonance Imaging (MRI) of blood volumechanges in human visual cortex during visual stimulation, investigatorshave demonstrated increases of up to 30% in cerebral blood volume(Belliveau et al., Science 254:716, 1991).

Optical images were obtained from this same cortical region (i.e., areaof interest) while the patient viewed blank slides and while namingobjects on slides presented every two seconds. Percentage differencemaps obtained during naming showed activation of the premotor area. Thesites of speech arrest and palate tingling were identified by surfacestimulation and demonstrate optical signals going in the oppositedirection. The area of activation was clearly different from that evokedby tongue movement without speech production. The optical images ofpremotor cortex activation during naming were in similar locations tothe cortical areas identified in PET single word processing studies(Peterson, et al., Nature 331:585, 1991; and Frith et al., J.Neuropsychologia 29:1137, 1991). The optical changes were greatest inthe area of the cortex traditionally defined as Broca's area and not inareas where electrical stimulation caused speech arrest.

FIG. 8 illustrates an optical map of a cortical area important forlanguage comprehension (Wernicke's area) in an awake human. FIG. 8 imageA shows the cortical surface of a patient where the anatomicalorientation is left-anterior, bottom-inferior, with the Sylvan fissurerunning along the top. After optical imaging, all cortical tissue to theleft of the thick black line was surgically removed. Sites #1 and #2were identified as essential for speech (e.g. cortical stimulationblocked ability of subject to name objects). At site #3, one namingerror in 3 stimulation trials was found. As the surgical removal reachedthe area labeled by the asterisks on the thick line, the patient'slanguage deteriorated. All the unlabeled sites in FIG. 8A had no errorswhile naming slides during cortical stimulation. FIG. 8, image B showsan overlay of a percentage difference image over the gray-scale image ofthe cortex acquired during a language naming trial (see description forFIG. 6 for description of the language naming trial). The magnitude ofthe optical change is shown in the gray-scale bar on the lower right ofthe image. This image demonstrates how a surgeon might use thisinvention intraoperatively to map language cortex.

FIG. 9 illustrates a time course and magnitude of dynamic opticalchanges in human cortex evoked in Wernicke's area (languagecomprehension). FIG. 9A shows plots of percentage change in opticalabsorption of tissue within the boxed regions shown in FIG. 8. FIG. 9Ashows plots of boxes 1 and 2 overlay essential language sites, and boxeslabeled 4, 5, and 6 overlay secondary language sites. Each of these fivesights showed significant changes occurring while the patient wasengaged in a language naming task. FIG. 9B show percentage changes fromthe six unlabeled boxes shown in FIG. 8. There were no significantincreases or decreases within these anterior sites. These datademonstrate that optical imaging can also identify both essential andsecondary language areas that must be preserved during neurosurgicalprocedures.

EXAMPLE 2

This example illustrates indocyanine green imaging of a low grade tumor.A MRI scan was conducted before the operation. Additionally, the patientwas investigated for tumor tissue using the apparatus describedaccording to the invention and specifically used in Example 1.

An averaged control image was obtained of the particular corticalsurface area of interest. Indocyanine green dye was administered into aperipheral intravenous catheter as a bolus at time 0. FIG. 10illustrates differential dynamics of dye to identify low grade human CNStumor. This series of images are from a patient having a low grade CNStumor (astrocytoma, grade 1). In FIG. 10A (upper left) the letteredlabels placed upon the brain by the surgeon overlay the tumor asidentified intraoperatively by ultrasound. However, tumors of this typeand grade are notoriously difficult to distinguish from normal tissueonce the surgical removal of the tumor has begun. FIG. 10B (middle left)shows a difference image taken approximately 15 seconds afterintravenous injection of dye (indocyanine green at 1 mg/kg). FIG. 10C(lower left) shows the difference image about 30 seconds after dyeadministration. The area of the tumor tissue showed the first tissuestaining. FIG. 10D (top right) shows that in this low grade tumor, alltissue (both normal and abnormal) showed staining at 45 sec after dyeadministration. FIG. 10E (middle right) is one minute after dyeadministration and FIG. 10F is five minutes after dye administration(showing complete clearance in this low grade tumor). These data showthat indocyanine green enters low grade tumor tissue faster than normalbrain tissue, and may take longer to be cleared from benign tumor tissuethan normal tissue. Therefore, it is possible to image even low gradetumors with this apparatus. Furthermore, it is possible to distinguishintraoperatively low grade tumor tissue from surrounding normal tissue.

Therefore, it is possible to image even low grade tumors by theinventive apparatus. Subsequent pathology of this tumor tissueestablished it as a low grade glioma.

EXAMPLE 3

This example illustrates the image of a highly malignant CNS tumor(glioblastoma). A patient was imaged in a neurosurgical procedure asdescribed in Example 1. The tumor imaging procedure was the same as inExample 2. The series of images in FIG. 11 are from the cortex of apatient with a malignant CNS tumor (glioblastoma; astrocytoma, GradeIV). FIG. 11A (upper left) shows a gray-scale image in which malignantbrain tumor tissue was densest in the center and to the right butelsewhere was mostly normal tissue (as was shown by pathology slidesavailable one week after surgery). FIG. 11B (middle left) is thedifference image at 15 seconds after intravenous injection ofindocyanine green, showing the dynamics of dye perfusion in the firstseconds in malignant tissue are similar to those in the first fewseconds of benign tumor tissue (see FIG. 11C). FIG. 11C (lower left)shows that at 30 seconds the malignant tissue is even more intense bycomparison to the normal tissue. FIG. 11D (upper right, 1 minute afterdye injection) and 11E (lower right, 10 minutes after dye injection)show that unlike benign tumor tissue, in malignant tumor tissue, dye isretained significantly longer, and in some cases, continues to sequesterin the malignant tumor tissue over longer periods of time. Therefore, itis possible with this device to identify malignant tumor tissue,distinguish intraoperatively between normal and malignant tumor tissue,and to distinguish between the various grades of tumor (e.g., normal vs.benign vs. malignant). Thus, it is possible to not only image thelocation of tumor tissue, but also to grade the tumor with moremalignant tumors retaining dye for a longer period of time than a lowergrade tumor.

EXAMPLE 4

This example illustrates a series of images taken after resection of amalignant CNS tumor until the tissue appeared to be normal. This type ofimaging of tumor margins provides a novel method of real-time imaging oftumor margins. After resection, the surgeon performed multiplehistological margin sampling and when waiting for frozen sectionresults, the images shown in FIG. 13 were obtained. FIG. 13 shows aseries of images and difference images of an area of interest where atumor was resected. The area of interest was thought to be free of tumortissue after the surgical removal of the tumor. Normally, in this sizeof a resection margin, only a single frozen sample would be taken forpathology analysis. For the purpose of this study, five biopsies weretaken from the margin to aid in correlating the histology with the mapobtained by the invention. FIG. 12A (top left) shows a gray-scale imageof the tumor margin. FIG. 12B shows the margin with labels that thesurgeon placed directly on brain. The purpose of these labels were toidentify where the surgeon was going to remove biopsy samples forhistological analysis after difference images were acquired with theinventive device. FIG. 12C (lower left) shows the difference image 1minute after intravenous injection of dye and FIG. 12D (lower right)shows the difference image 10 minutes after dye injection. Thesepost-dye difference images reveal a number of sights that contain tumortissue as well as areas of normal tissue. The accuracy of the opticalimaging was confirmed post operatively by analysis of the biopsies. Notethat a small area on the lower right of FIG. 12D indicates a possibleregion of tumor tissue that would not have been biopsied by the surgeon.Hence, even in the case of extensive biopsy, the sampling error exceedsthe accuracy of the invention. These data show that the invention isable to identify small remnants of tumor tissue in a tumor margin afterresection of a tumor. As well, the invention could act as an aid toremoving biopsies from the site of a tumor margin and reduce the innatesampling error associate with the presently used random samplingtechnique.

EXAMPLE 5

This example illustrates a means for setting the CCD to optimize theapparatus to be able to detect signal with maximum sensitivity across afull dynamic range. The CPU should be programmed with software havingthe following features: (1) an output-analog signal, values of the imageare close to saturating on the bright end (i.e., close to 225) aredisplayed as a distinct color, such as red; (2) values that are close tothe dark end (i.e., are close to zero) are also displayed as a distinctcolor, such as blue. The following procedure is an example of anadjustment of the CCD camera.

1. With the gain and black-level on a camera-control box (CCB) initiallyset to 0, increase the emr intensity until the video signal is justsaturating on the bright-end (i.e., some values in the output-analogsignal can be seen to be close to 255).

2. Increase the black-level on the CCB until the output image can beseen to be saturating on the dark end (i.e., some values in the outputanalog image can be seen to be close to 0).

3. Increase the gain on the CCB until some values of the output analogimage can be seen to be saturating on the high end.

4. Iterate steps (2) and (3) until either (a) the gain is set to itsmaximum possible value, or (b) the black-level is set to its maximumpossible value, or (c) the image is maximally enhanced across is fulldynamic range (that is, no further adjustments of CCB gain, black-levelor emr source will improve the image.

5. If in step (4) (a), the gain is set to its maximum level, or (b) theblack-level is set to its maximum level, but the output image is stillnot maximally enhanced, then in the case of (a), decrease the setting onthe CCB gain slightly, increase the emr source intensity until justsaturating the bright end, and return to step (2). In the case of (b),decrease the setting of the black-level slightly, decrease the emrintensity, and return to step (3).

EXAMPLE 6

This example illustrates a series of experiments using a rat gliomamodel intraoperatively to investigate whether the inventive method andinventive device could function in an operating room setting to providereal time information to the surgeon regarding resection of all tumortissue. The rat glioma model is a standard predictive model and was usedto delineate dye uptake, clearance and overall parameters of opticalimaging that result in the best images. The advantages of this model arethe ability to consistently get reproducible tumors for imaging studiesand to be able to resect tumor under an operating microscope and stillfind residual tumor with the inventive optical imaging. A disadvantageof this model is the more sarcoma-like appearance of the tumor and alesser degree of vascularity compared to human gliomas.

Briefly, the rat glioma model uses an ethylnitrosourea-induced F-344 rattumor line developed from a clonal population of a spinal malignantastrocytoma. This tumor is similar to human astrocytomas microscopicallyand in vivo, because both have stellate-shaped cells in the brainparenchyma and both have introcytoplasmic filaments 80-100 mm indiameter as seen by scanning electron microscopy. The glioma cells weremaintained in Weymouth's medium supplemented with 10% fetal calf serum.Viable cells (5×10⁴) were trypsinized from a monolayer culture andimplanted stereotaxically into the right cerebral hemisphere of 30syngeneic female rats, each weighing 140-160 g. The stereotaxiccoordinates for right frontal lobe implantation were 4.5 mm anterior tothe frontal zero plane, 3 mm right from the midline and 6 mm deep. Therats were anesthetized for implantation. The heads were shaved andscalps opened, and a 1 mm burr hole made at the appropriate coordinates.The cells were injected through a 27 gauge needle, the needle left inplace for 30 sec post injection and the hole was covered with bone wax.The scalp was sutured and the animals observed for 3-4 hrs until theyreturned to normal activity and feeding. The animals were used 10-14days after tumor implantation. In this model, animals begin to showclinical symptoms from the tumor by 16-19 days, such as decreasedactivity and feeding, hemiparesis and eventually succumb between 19-27days from mass effects due to tumor expansion.

Fourteen animals underwent complete study, including imaging before andafter resection of the tumor. For study, the animals were anesthetizedwith 2% isoflurane, and the femoral vein canulated for administration ofthe dye. Anesthesia was maintained with a-chloralsoe (50 mg/kgadministered ip) and urethane (160 mg/kg administered ip). The animalswere placed in a stereotaxic holder. Imaging studies were then carriedout before (see example 7 below) or after removal of the cranium. Thetumor typically occupied the anterior one half to two thirds of theright hemisphere exposure. The compressed brain without any tumorinfiltration was defined as the tumor surround to separate it from thenormal hemisphere on the contralateral side. Indocyanine green was usedas the intravenous dye. No dye was found in the cerebrospinal fluidafter administration.

The cortical surface was first imaged, and then an operating microscopewas used to attempt gross total removal of the tumor. Sites were thenchosen for biopsy based on optical imaging results and later analyzedhistologically. The biopsy specimens were fixed in 10% paraformaldehyde,Nissl stained and mounted. AU specimens were read blindly and labeledeither positive or negative for tumor. These data were correlated to theoptical imaging results to identify residual tumor and statisticalanalysis (Chi square or student t-test) performed to determine thesignificance of the results.

The imaging apparatus is described herein. Light was from atungsten-halogen bulb regulated by a D.C. power supply, passed through alongpass filter (690 nm), and through a right angled prism reflectedthrough a 50 or 100 mm objective lens onto the cortical surface. Thereflected light was collected by the same objective lens and focused bya projection lens onto the surface of a CCD camera (COHU 6300). Theimaging apparatus was attached to the stereotaxic frame which wasrigidly fixed to a vibration isolation table. Specially designedautomatic warping algorithms were designed to compensate for smallamounts of movement. Images (512×480 pixels) were acquired at 30 Hz anddigitized at 8 bits (256 gray levels). Every 2 sec, a single imagecomprising 30 averaged frames was collected (1 sec) and then stored (1sec). Control images were collected prior to injection of the dye andthen for 2 min after dye injection. The dye injection was made over a 1sec period while the last control image was being stored. A period of 20min was allowed between dye injections to allow optical images to returnto baseline. The initial control images of each trial were subtractedfrom each other to insure that the baseline starting point of each trialwas equivalent.

A single control image was chosen and then subtracted from each of thecontrols (4-6 images) and each of the post-dye injection images. Theresultant image was divided by the original control image and multipliedby 100 to give a composite percentage difference for the entire sequencebefore and after dye injection. The optical change that occurred betweenseparate control images was 0.2-0.7%, whereas the peak changes resultingfrom dye injection were in the range of 5-40%. The control percentagedifference image are represented in the attached figures. The spatialresolution of an individual pixel in the image ranged from 13.5×11.7 mm²to 27×25.4 mm². Boxes measuring from 15-30 pixels per side were drawn onthe images. The average percentage change in the individual boxes wascalculated and used to demonstrate graphically the optical changes overtime in the different types of tissue.

Imaging studies were performed on fourteen animals. The time course ofdye perfusion through the tissue had a dynamic aspect. Optical imagingof indocyanine green dye perfusion at a dose of 1 mg/kg in 16 separateruns from a cortical surface in 9 different animals demonstrated thedynamic nature of the optical changes. FIG. 16 show a sequence of imagesto illustrate the dynamic differences of dye absorption changes betweentumor and non-tumor tissue. FIG. 16A shows a gray-scale image of thearea of interest. This is the same animal as shown in FIG. 16, howeverthe cranium has now been removed so as to expose the left hemispherecontaining the glioma, and the right hemisphere containing normaltissue. Box 1 overlays the tumor, Box 2 the tumor-surround, and Box 3overlays normal tissue. FIG. 16B shows the difference image of the areaof interested 1 second after 1 mg/kg of indocyanine green had beenintravenously injected into the animal. During this initial time, thetumor tissue is the first to show a measurable optical change indicatingthe uptake of dye occurs first in the tumor tissue. The gray-scale barindicate the relative magnitude of the optical changes in the sequenceof difference images. FIGS. 16C and 16D show difference images of thearea of interest 4 seconds and 30 seconds respectively after dyeinjection. At these intermediate stages dye appears to collect in bothnormal and tumor tissue. FIGS. 16E and 16F show difference images of thearea of interest 1 minute and 5 minutes respectively after injection ofdye. At these later times, it becomes clear that dye is still collectingin tumor tissue even thought it is being cleared from normal tissue.

The peak optical changes occur after 6 seconds from dye administration,but after the normal hemisphere begins to clear the dye, the tumortissue continues to maintain a large optical due to a lack of dyeclearance. These changes localize anatomically to the tumor site.

The optical signals begin to change within the first 2-3 seconds afterdye injection and peak 6 seconds after injection in all three areas,tumor tissue, tumor surround and normal brain. However, the threedifferent tissue types are differentiated by the rate of rise over thefirst four seconds, the peak optical change reached, and the eventualplateau that occurs after the first 30 seconds. The tumor tissue had asignificantly different peak percentage difference change (40.5±9.6%)than either the tumor surround (16.4±6.8%) or the normal brain(9.7±4.7%).

FIG. 17 is a plot of an average of the percentage change in emrabsorption average over the spatial areas indicated by boxes 1, 2, and 3from FIG. 16A. The increase in absorption is a function of theconcentration of dye in the tissue at a particular time. The graphlabeled "tumor tissue" is a plot of the dynamics of the absorptionchanges within box 1 from FIG. 16A. The graph labeled "tumor surround"is a plot of the dynamics of the absorption changes within box 2 fromFIG. 16A. The graph labeled "normal brain" is a plot of the dynamics ofthe absorption changes within box 3 from 16A. This data as well as thatfrom FIG. 16 show that the inventive method and device is able todistinguish not only tumor from non-tumor tissue, but alsotumor-surround areas which contain varying densities of tumor vs. normalcells.

Since the peak optical change was always reached 4-6 seconds after dyeinjection, there was also a significantly faster rate of optical changein the tumor tissue compared to the tumor surround or the normal brain.A more rapid onset of dye perfusion into the tumor tissue was displayedas a faster time course. The tumor tissue had a more rapid and greaterrise time than either the tumor surround or normal brain (p<0.01).

An essential feature to be able to differentiate normal from abnormaltissue is distribution of the dye into three very different tissuecompartments. In 13 of 14 animals there was a prolonged increase (>2min) in the optical signal in the tumor after the normal and tumorsurround tissue had returned to baseline. Finally, even the normal andtumor surround tissue were significantly different in dye uptake (risetime: normal 2.4%/sec; tumor surround 4.0%/sec). Therefore, the dynamicfeatures of dye uptake and clearance are critical for determining thetype of tissue involved in imaging resection margins.

The rat glioma model also provided an opportunity to look at imagingresection margins once all visible tumor had been removed. FIG. 18Ashows a higher magnification image of the left hemisphere tumor marginof the animal after the tumor had been resected. Boxes 1 are over areasthat contained small traces of residual tumor cells, and boxes 2 areover areas that contained only normal tissue. The gray-scale barindicates the magnitude of optical change in the difference images.FIGS. 18B, 18C, and 18D show difference images of the tumor margin 4,30, and 60 seconds after intravenous dye injection respectively. Minutebiopsies were taken from areas that showed preferred dye containment andfrom areas from which the dye cleared rapidly. These biopsies wereanalyzed blindly and later correlated to the location from which thebiopsies were taken. Those biopsies taken from areas which cleared dyewere shown to contain only normal cells, whereas biopsies taken fromareas which sequestered dye were shown to contain tumor cells. The morerapid rate of rise seen in cortical surface imaging was still presentfor the resection margins that were positive for tumor compared tonormal brain. Again, significant differences between the tumor and thenormal brain existed for the rate of rise, peak optical change, andplateau 60 seconds after dye injection (all p<0.01). FIGS. 15-18demonstrate that the inventive method and device can be used incombination with multiple injections of dye for repeated applicationthroughout a tumor resection surgery (in this case, 4 separateinjections of dye were given). Furthermore, extremely small islands ofresidual tumor can be mapped within the tumor margins.

Sensitivity and specificity of optical imaging was determined for 34samples (n=12 animals). Of 15 biopsy sites deemed negative for tumor byoptical imaging, 14 of 15 were clear of tumor by histological analysis(sensitivity 93%). Most of the specimens that were negative for tumorwere taken from the posterior wall of the tumor resection cavity or thedepth of the cavity (where the hippicampus or denate gyrus werefrequently biopsied). Of 19 biopsy sites deemed positive for tumor byoptical imaging, 17 of the biopsy specimens were read as positive fortumor (specificity 89.5%). The two sites that were negative for tumor onhistology but positive for tumor by optical imaging had increasedcellularity but were deemed negative for tumor because there was nofocus of tumor tissue present. The overall significance of these resultsare p<0.001.

FIG. 19 shows dynamic information of dye uptake and clearance in tumorvs. non-tumor tissue. This is a plot of an average of the percentagechange in emr absorption average over the spatial areas indicated byboxes 1 and 2 from FIG. 18A. The increase in absorption is a function ofthe concentration of dye in the tissue at a particular time. The graphlabeled "margins tumor" is a plot of the dynamics of the absorptionchanges within box 1 from FIG. 18A. The graph labeled "margins normal"is a plot of the dynamics of the absorption changes within box 2 fromFIG. 18A. This data as well as that from FIG. 19 show that the inventivedevice and method are able to distinguish tumor from non-tumor tissuewithin tumor margins with extremely high spatial and temporalresolution.

EXAMPLE 7

This example illustrates a series of experiments using a rat gliomamodel through an intact skull to investigate whether the inventivemethod and inventive device could function in to image tumor tissuethrough an intact skull and through intact skin prior to or aftersurgery. Far red wavelengths of emr are known to penetrate through boneand skin. Imaging of tumor tissue was attempted through the intact skullof the rat. The extent of tumor identified was not as accurate as withthe cortex exposed, however, the area lying beneath the skull with tumortissue was easily identified, localized and continued to concentrate dyeafter several minutes. Initially, after dye injection, the area of thetumor demonstrated a much larger signal than the normal brain of thecontralateral hemisphere. One minute after dye injection, the dye hadbeen cleared from the normal brain and the only residual signal remainedin tumor tissue and the sagital/transverse sinuses.

FIG. 14A is a gray-scale image of the cranial surface of a rat. Thesagital suture runs down the center of the image. Tumor cells had beeninjected into the left side some days earlier so that this animal haddeveloped a glioma on the left hemisphere of its brain. The righthemisphere was normal. Box 1 lays over the suspect region of braintumor, and box 2 lays over normal tissue. FIG. 14B is a difference image1 second after indocyanine green dye had been intravenously injectedinto the animal. The region containing tumor tissue becomes immediatelyvisible through the intact cranium. FIG. 14C shows that 5 seconds afterdye injection the dye can be seen to profuse through both normal andtumor tissue. FIG. 14D shows that 1 minute after dye injection, thenormal tissue has cleared the dye, but dye is still retained in thetumor region. The concentration of dye in the center of this differenceimage is dye circulating in the sagital sinus.

The time course of optical changes imaged through the cranium from tenruns in four animals are shown in FIG. 15. The optical changes weredetermined by the average optical change in a box placed directly overthe tumor and over the normal hemisphere. The increase in absorption isa function of the concentration of dye in the tissue at a particulartime. The graph labeled "extracranial tumor" is a plot of the dynamicsof the absorption changes within box 1 from FIG. 14A. The graph labeled"extracranial: normal" is a plot of the dynamics of the absorptionchange within box 2 from FIG. 14A. The peak optical changes for thetumor imaged through the cranium were 13.1±3.9% and this wassignificantly greater compared to normal brain of 7.8±2.3% (p<0.01). Theplateau phase 60 seconds after dye injection was also significantlygreater in tumor tissue (40.5±9.6%) compared to normal brain (3.1±0.7%)(p<0.01).

EXAMPLE 8

This example illustrates a rat model showing activation of sensorycortex by stimulation of a peripheral nerve. More specifically, afferentsensory input was generated in an anesthetized rat by directlystimulating the sciatic nerve. In FIG. 5, the leftmost image is agray-scale image of hind limb somatosensory cortex in an anesthetizedrat. The magnification is sufficiently high so that individualcapillaries can be distinguished (the smallest vessels visible in thisimage). The center image is an image of a percentage difference controloptical image during rest. The magnitude of optical change is indicatedby the gray-scale bar in the center of this image. The arrow beside thisgray-scale indicates the direction of increasing amplitude. Therightmost image is a percentage difference map of the optical changes inthe hind limb sensory cortex during stimulation of the sciatic nerve.Therefore, it is possible to utilize the inventive device and method tomap functional areas of the cortex corresponding to different areas ofthe body.

EXAMPLE 9

This example illustrates that differential dynamics of dye uptake andretention can characterize and identify tumor tissue in human patientsthat do not contrast enhance with traditional MRI imaging. A proportionof non-benign tumors are not observable with present MRI imagingtechniques. The images in FIG. 13 are from a patient whose tumor did notcontrast enhance with MRI. This lack of enhancement is usually typicalof benign tumors. However, optical imaging was able to identify thistumor as a non-benign type (an anoplastic astrocytoma). FIG. 13A showsthe gray-scale image of the area of interest. FIG. 13B shows thedifference image prior to dye injection. FIG. 13C shows the area ofinterest 1 minute after intravenous dye injection, and FIG. 13D showsthe area of interest 5 minutes after dye injection. Note that the dye isretained in this tissue for a significant time. As shown in FIGS. 10,11, and 12, this dynamic trait is a characteristic of a non-benigntumor.

EXAMPLE 10

This example illustrates imaging of functional regions of peripheralnerves. A rat is anesthetised and dissected to expose the sciatic nerve.Using silver electrodes we electrically stimulate the caudal end of thenerve while acquiring a first sequence of difference images. We note theextent of the spread of the intrinsic optical changes in the nerve fromthe point of stimulation by examining the difference imaging containingthe peak optical change from the control. Next, we make a crush in thenerve at a small distance anterior to the stimulating electrodes. Weacquire a second sequence of difference images and compare thecorresponding difference image from this sequence to the imagecontaining the peak optical change from the first image. We note thatthe intrinsic optical changes diminish abruptly at the point where thenerve was damaged.

Finally, we stimulate the nerve anteriorly to where the crush was madeand after acquiring a third sequence of difference images, we again notewhere the intrinsic changes abruptly end. This method allows us tolocalize the location and extent of damaged or disfunctional peripheralnerve tissue.

EXAMPLE 11

This example illustrates imaging of functional regions of Cranial NerveVIII. Cranial nerve VIII (Vestibulocochlear nerve) is exposed. Soundtones provide the auditory stumulus which eventually cause activation ofthis nerve. A sequence of difference images before, during, and afterthe appropriate auditory stimuli are applied show that intrinsic opticalchanges of the nerve are associated with its functional activiation.Next, a small region of this nerve is damaged by crushing. A secondsequence of images reveal that auditory stimulation evokes intrinsicoptical changes in the nerve up to the point of damage.

EXAMPLE 12

This example illustrates various methods for enhancing images obtainedfrom tumor tissue or intrinsic signal difference images using multiplewavelength and/or laser illumination, and a method for extracting 3-Dinformation using multiple wavelengths. We expose a region of cortex inan anesthised rat. First, illuminating with white light from a tungstenfilament lamp, we acquire a sequence of difference images prior to,during, and following electrical stimulation of this region of cortexwith bipolar stimulating electrodes. Next, we acquire second and thirddifference image sequences, following the identical procedure as we didfor the first sequence, except that in the second sequence, the cortexis illuminated with with 690 nm and in the third sequence 510 nm light.The change in wavelengths is accomplished by placing 690±10 nminterference filter or a 510±10 nm interference filter between thelightsource and the brain.

We compute the contrast-enhanced image by first ratioing a control 690nm image with a control 510 nm image. Second, we ratio a 690 nm imageduring stimulation with the corresponding 510 nm image. We then combinethe ratio images to compute the percentage difference image. In thismanner, the noise has been significantly reduced, hence the signal/noiseratio has been significantly increased.

Next, we show how to extract depth information from the multiplewavelength images that we have acquired. Longer wavelength lightpenetrates to a greater depth through the cortex, and shorter wavelengthlight to a lesser extent. Hence, the 690 nm image as penetrated cortexto x mm, and the 510 nm image to y mm where x<y

We subtract the 610 nm image from the 510 nm image, showing an "opticalwedge" containing information from a depth of (x-y) mm to x mm withinthe cortical tissue. By using a series of other interence filters, weacquire a sequence of images containing information from many differentdepths of the cortex. It is possible to acquire 3-dimentionalinformation.

Next, exposing tumor tissue in a rat in which we have induced tumorgrowth, we repeat all of the above experiments showing that in a likemanner, we can improve signal/noise and extract 3-dimentionalinformation in tumor tissue. However, instead of stimulating the tissueelectrically, we inject the dyes indocyanine green or Evans blue.

Finally, we repeat the above experiments by illuminating the cortex atseveral different wavelengths with a dye-tunable laser (a coherentsource) instead of with the non-coherent tungsten filament lamp. Withthe laser (or any coherent source) we have the additional advantage inthat we can separate out the components of the signal due to changes inreflection or scattering. By illuminating the cortex with the laserdirectly parallel to the camera (both of which are perpendicular to thebrain), we are imaging reflected light only. By moving the laser at anangle θ to the camera, we are measuring changes due to scattering aloneat this particular angle.

EXAMPLE 13

This example illustrates a C code to implement the inventive algorithmand strategy for automatically translating a pair of images to the bestfit with a control image with translation in the x-y plane. One canimplement the algorithm so that the inventive device can automaticallytranslate subsequently acquired images to the control image so thatmotion will be compensated in an on-line fashion in the operating room.As well, it is clear the this algorithm can be implemented in integerarithmetic so that it is computationally efficient. Also, since most ofthe memory required for this algorithm can be dynamically allocated,this algorithm makes efficient use of memory.

This program automatically translates two images stored in the ImagingTechnology 151 frame buffers to a best fit according to whichtranslation minimizes the variance of the subtracted images over theuser-selected areas of interests. The user specifies an image for framebuffer B1 and then specifies an image for frame buffer ALOW to beautomatically translated to the B1 image. If the number of areas ofinterest are less than 9 and the search depth is less than 8 then allthe data can be read from the frame buffer into the host computer's RAM.This allows for speed and reduces the IO to the frame buffer. Thisprogram can be easily altered to use integer arithmetic only for allcalculations.

This program is compilable under Microsoft's C/C++ Version 7.0 compilerlinked with Imaging Technology's ITEX run-time library. It will run on aPC 486 compatible host computer controlling a 1k×1k frame buffer, anADI, and an ALU from Imaging Technology's ITEX 151 series hardware.

    __________________________________________________________________________    #include <stdio.h>                                                            #include <math.h>                                                             #include <stdlib.h>                                                           #include <conio.h>                                                            #include <itex150.h>                                                          #include <graph.h>                                                            #include <float.h>                                                            #include <dos.h>                                                              #define MEM.sub.-- CHUNK   20                                                 #define QUIT    -1                                                            #define GO      -2                                                            #define RADIX.sub.-- 10   10                                                  #define RETURN    13                                                          #define ESC    27                                                             #define CURSOR.sub.-- UP   72                                                 #define CURSOR.sub.-- DOWN   80                                               #define CURSOR.sub.-- RIGHT  77                                               #define CURSOR.sub.-- LEFT  75                                                #define CURSOR.sub.-- JUMP.sub.-- UP  141                                     #define CURSOR.sub.-- JUMP.sub.-- DOWN  145                                   #define CURSOR.sub.-- JUMP.sub.-- RIGHT 116                                   #define CURSOR.sub.-- JUMP.sub.-- LEFT 115                                    struct data.sub.-- box{                                                        int x,y;                                                                      int height, width;                                                           };                                                                            typedef struct data.sub.-- box data.sub.-- box;                               int box.sub.-- count = 0;                                                     int depth = 10;                                                               void init.sub.-- box.sub.-- overlay(void);                                    BYTE **ram.sub.-- boxdata(data.sub.-- box **map.sub.-- ptr, int               search.sub.-- depth,int                                                          frame.sub.-- buffer);                                                      data.sub.-- box **define.sub.-- boxmap(void);                                 data.sub.-- box *draw.sub.-- boxes(void);                                     BYTE **diff.sub.-- box(data.sub.-- box **map.sub.-- ptr, BYTE **fb1, BYTE     **fb2,                                                                           int box,int x.sub.-- off, int y.sub.-- off);                               float sub.sub.-- rects(data.sub.-- box **map.sub.-- ptr, BYTE                 **diff.sub.-- rects, int search.sub.-- depth,                                    int ptr.sub.-- place);                                                     BYTE ***diff.sub.-- map(data.sub.-- box **map.sub.-- ptr,int fb1; int         fb2, int search.sub.-- depth);                                                float *sum.sub.-- rects(data.sub.-- box **map.sub.-- ptr, BYTE                ***diff.sub.-- rects, int search.sub.-- depth                                    ,int av.sub.-- flag);                                                      int *min.sub.-- boxset(data.sub.-- box **map.sub.-- ptr, float                *float.sub.-- ptr, int search.sub.-- depth);                                  float *var.sub.-- rects(data.sub.-- box **map.sub.-- ptr, BYTE                ***diff.sub.-- rects, int search.sub.-- depth,                                   int av.sub.-- flag);                                                       data.sub.-- box **define.sub.-- boxmap(void)/* returns a pointer to an        array of boxes */                                                               int i = 0, maxbox = MEM.sub.-- CHUNK; /* dynamically allocate                  mem in 20-box */                                                              int error.sub.-- flag = 0, inchar;   /*    chunks.   */                       data.sub.-- box **group.sub.-- of.sub.-- boxes;                               data.sub.-- box *box.sub.-- pointer;                                          int watch = GO;                                                               box.sub.-- pointer = (data.sub.-- box *)malloc(maxbox*sizeof(data.sub.-    - box));                                                                           if(box.sub.-- pointer == NULL)                                                printf("\nTROUBLE AT 70\n");                            group.sub.-- of.sub.-- boxes = (data.sub.-- box **)malloc(maxbox*sizeof    (data.sub.-- box *));                                                            if (group.sub.-- of.sub.-- boxes == NULL)                                       printf("\nTROUBLE AT 74\n");                                               /* FLAG 1 |||*/                                            else{                                                                           while (watch |= QUIT){                                                         if(error.sub.-- flag == 0) {                                                    printf("\n\nType ESC use draw.sub.-- boxes        nn");                                                                                 inchar = getch();                                                             if(inchar == 0)                                                                 getch();                                                                    if(ESC == inchar){                                                              box.sub.-- count = 1;                                                         init.sub.-- box.sub.-- overlay();                                             while( watch |= QUIT){                                                         if (i >= maxbox){                                                              maxbox += MEM.sub.-- CHUNK;                                                   group.sub.-- of.sub.-- boxes = (data.sub.-- box                   **)realloc(group.sub.-- of.sub.-- boxes,                                                        maxbox*sizeof(data.sub.-- box *));                                      if (group.sub.-- of.sub.-- boxes == NULL)                                     printf("\nTROUBLE AT 91\n");; /* FLAG 2       |||*/                                                                                     }                                                                             group.sub.-- of.sub.-- boxes i++! = draw.sub.-- boxes();                      printf("Do you want to draw box number %d ?\n",                         (box.sub.-- count + 1));                                                  inchar = getch();                                                             if(inchar==0)                                                                   getch();                                                                    if (inchar == ESC)                                                              box.sub.-- count++;                                                         else                                                                            watch = QUIT;                                                              }                                                                            }                                                                            }                                                                            }                                                                       }                                                                             if(i < maxbox){                                                                 group.sub.-- of.sub.-- boxes = (data.sub.-- box **)realloc(group.sub.-    - of.sub.-- boxes,                                                                               (i)*sizeof(data.sub.-- box *));                                if (group.sub.-- of.sub.-- boxes == NULL)                                       printf("\nTROUBLE AT 113\n");                         }          /* FLAG 3 ||||*/                                                   return(group.sub.-- of.sub.-- boxes);                                       }                                                                             /***********************                                                      draw.sub.-- boxes() is a simple function that draws boxes on the image        for                                                                           the user to view using the overlay capabilities of the ITEX 151               ADI overlay capability. Its main function is to return a pointer              to data.sub.-- box structure containg the information for the location        and                                                                           area of the selected area of interest                                         *****************************************/                                    int curs.sub.-- x = 235, curs.sub.-- y = 220;                                 data.sub.-- box *draw.sub.-- boxes(void)                                      {                                                                              int dcurs.sub.-- x,dcurs.sub.-- y;                                            int x.sub.-- start, y.sub.-- start, x.sub.-- length, y.sub.-- length;         int text.sub.-- char,k;                                                       char box.sub.-- number 3!;                                                    data.sub.-- box *box.sub.-- pointer;                                          box.sub.-- pointer = (data.sub.-- box *)malloc(sizeof(data.sub.--            box));                                                                           if(box.sub.-- pointer == NULL)                                                  printf("\nTROUBLE AT 131\n");                        line(B2,0,curs.sub.-- x-4,curs.sub.-- y,curs.sub.-- x+4,curs.sub.--           y,1);                                                                         line(B2,0,curs.sub.-- x,curs.sub.-- y-5,curs.sub.-- x,curs.sub.--             y+5,1);                                                                       adi.sub.-- lutmode(DYNAMIC);                                                  k = 0;                                                                        x.sub.-- start = y.sub.-- start = x.sub.-- length = y.sub.-- length = 0;      while(1) {                                                                      text.sub.-- char=getch();                                                     if(text.sub.-- char == RETURN) {                                                ++k;                                                                          dcurs.sub.-- y=0;                                                             dcurs.sub.-- x=0;                                                             if (k == 1){                                                                    x.sub.-- start = curs.sub.-- x;                                               y.sub.-- start = curs.sub.-- y;                                               line(B2,0,curs.sub.-- x-4,curs.sub.-- y,curs.sub.-- x+4,curs.sub.--     y,0);                                                                               line(B2,0,curs.sub.-- x,curs.sub.-- y-5,curs.sub.-- x,curs.sub.--       y+5,0);                                                                           }                                                                             if (k == 2)                                                                     break;                                                                    }                                                                             else if (text.sub.-- char == 0){                                                text.sub.-- char = getch();                                                   switch(text.sub.-- char){                                                       case CURSOR.sub.-- UP:                                                          dcurs.sub.-- y=-1;                                                            dcurs.sub.-- x=0;                                                             break;                                                                      case CURSOR.sub.-- DOWN:                                                        dcurs.sub.-- y=1;                                                             dcurs.sub.-- x=0;                                                             break;                                                                      case CURSOR.sub.-- LEFT:                                                        dcurs.sub.-- y=0;                                                             dcurs.sub.-- x=1;                                                             break;                                                                      case CURSOR.sub.-- RIGHT:                                                       dcurs.sub.-- y=0;                                                             dcurs.sub.-- x=1;                                                             break;                                                                      case CURSOR.sub.-- JUMP.sub.-- UP:                                              dcurs.sub.-- y = -7;                                                          dcurs.sub.-- x = 0;                                                           break;                                                                      case CURSOR.sub.-- JUMP.sub.-- DOWN:                                            dcurs.sub.-- y= 7;                                                            dcurs.sub.-- x = 0;                                                           break;                                                                      case CURSOR.sub.-- JUMP.sub.-- LEFT:                                            dcurs.sub.-- y = 0;                                                           dcurs.sub.-- x = -7;                                                          break;                                                                    case CURSOR.sub.-- JUMP.sub.-- RIGHT:                                             dcurs.sub.-- y = 0;                                                           dcurs.sub.-- x = 7;                                                           break;                                                                      default:                                                                        dcurs.sub.-- x = 0;                                                           dcurs.sub.-- y = 0;                                                           break;                                                                    }                                                                           }                                                                             else                                                                          text.sub.-- char = -1;                                                        if((k == 0)&&(text.sub.-- char |= -1)) {                                        line(B2,0,curs.sub.-- x-4,curs.sub.-- y,curs.sub.-- x+4,curs.sub.--       y,0);                                                                             line(B2,0,curs.sub.-- x,curs.sub.-- y-5,curs.sub.-- x,curs.sub.--         y+5,0);                                                                           curs.sub.-- x=max(min(curs.sub.-- x+dcurs.sub.-- x,511),0);                   curs.sub.-- y=max(min(curs.sub.-- y+dcurs.sub.-- y,479),0);                   line(B2,0,curs.sub.-- x-4,curs.sub.-- y,curs.sub.-- x+4,curs.sub.--       y,1);                                                                             line(B2,0,curs.sub.-- x,curs.sub.-- y-5,curs.sub.-- x,curs.sub.--         y+5,1);                                                                         }                                                                             else if (k == 1) {                                                              line(B2,0,x.sub.-- start,y.sub.-- start,x.sub.-- start + x.sub.--         length,y.sub.-- start,0);                                                         line(B2,0,x.sub.-- start,y.sub.-- start,x.sub.-- start,y.sub.-- start     + y.sub.-- length,0);                                                             line(B2,0,x.sub.-- start,y.sub.-- start + y.sub.-- length,x.sub.--        start +                                                                                   x.sub.-- length,y.sub.-- start + y.sub.-- length,0);                  line(B2,0,x.sub.-- start + x.sub.-- length,y.sub.-- start,x.sub.--        start +                                                                                   x.sub.-- length,y.sub.-- start + y.sub.-- length,0);                  curs.sub.-- x=max(min(curs.sub.-- x+dcurs.sub.-- x,511),0);                   curs.sub.-- y=max(min(curs.sub.-- y+dcurs.sub.-- y,479),0);                   x.sub.-- length = curs.sub.-- x - x.sub.-- start;                             y.sub.-- length = curs.sub.-- y - y.sub.-- start;                             line(B2,0,x.sub.-- start,y.sub.-- start,x.sub.-- start + x.sub.--         length,y.sub.-- start,1);                                                         line(B2,0,x.sub.-- start,y.sub.-- start,x.sub.-- start,y.sub.-- start     + y.sub.-- length,1);                                                             line(B2,0,x.sub.-- start,y.sub.-- start + y.sub.-- length,x.sub.--        start +                                                                                   x.sub.-- length,y.sub.-- start + y.sub.-- length,1);                  line(B2,0,x.sub.-- start + x.sub.-- length,y.sub.-- start,x.sub.--        start +                                                                                   x.sub.-- length,y.sub.-- start + y.sub.-- length,1);                }                                                                           }                                                                              x.sub.-- start = min(x.sub.-- start,x.sub.-- start + x.sub.-- length);        y.sub.-- start = min(y.sub.-- start,y.sub.-- start + y.sub.-- length);        x.sub.-- length = abs(x.sub.-- length);                                       y.sub.-- length = abs(y.sub.-- length);                                       .sub.-- itoa(box.sub.-- count,box.sub.-- number,RADIX.sub.-- 10);             if((x.sub.-- length < 10) || (y.sub.-- length < 10))       {                                                                                if (x.sub.-- length > y.sub.-- length)                                           text(B2,0,x.sub.-- start+x.sub.-- length/2-7,y.sub.-- start-15,                    HORIZONTAL,1,1,box.sub.-- number);                                    else{                                                                            if(box.sub.-- count < 10)                                                       text(B2,0,x.sub.-- start-11,y.sub.-- start+y.sub.-- length/2-2,                  HORIZONTAL,1,1,box.sub.-- number);                                       else                                                                            text(B2,0,x.sub.-- start-18,y.sub.-- start+y.sub.-- length/2-2,                  HORIZONTAL,1,1,box.sub.-- number);                                    }                                                                             }                                                                             else                                                                          text(B2,0,x.sub.-- start+x.sub.-- length/2-5,y.sub.-- start+y.sub.--       length/2-2,                                                                              HORIZONTAL,1,1,box.sub.-- number);                                  box.sub.-- pointer->x = x.sub.-- start;                                                      /* x coordinate */                                             box.sub.-- pointer->y = y.sub.-- start;                                                      /* y coordinate */                                             box.sub.-- pointer->height = y.sub.-- length;                                                   /* vertical length */                                       box.sub.-- pointer->width = x.sub.-- length;                                                 /* horiz length */                                             curs.sub.-- x+=20;  /* move cross-hairs to nearby location */                 curs.sub.-- y+=20;                                                            return box.sub.-- pointer;                                                   }                                                                             void.sub.-- init.sub.-- box.sub.-- overlay(void)                              {          /* Clear B2, set path to B1, and overlay */                         fb.sub.-- setsmask(FRAMEB,0x00FF); /* B2 on B1                                                           */                                                 fb.sub.-- clf(B2,0);                                                          select.sub.-- path(B1);                                                       adi.sub.-- hbanksel(1);                                                       adi.sub.-- hgroupsel(RED);                                                    adi.sub.-- clearlut(250);                                                     adi.sub.-- hgroupsel(GREEN);                                                  adi.sub.-- clearlut(0);                                                       adi.sub.-- hgroupsel(BLUE);                                                   adi.sub.-- clearlut(0);                                                      }                                                                             void init.sub.-- itex.sub.-- stuff(void)                                      {                                                                              err.sub.-- level(2);                                                          load.sub.-- cfg("");                                                          initsys();                                                                   }                                                                             BYTE **ram.sub.-- boxdata(data.sub.-- box **map.sub.-- ptr,int                search.sub.-- depth, int                                                       frame.sub.-- buffer){                                                         int i,j,k,x.sub.-- start,x.sub.-- end,x.sub.-- length,y.sub.-- start,y.su    b.-- length;                                                                   BYTE **image.sub.-- rects;                                                    unsigned int count=0;                                                         unsigned int total.sub.-- length=0;                                           select.sub.-- path(frame.sub.-- buffer);                                      for(i=0;i<box.sub.-- count;i++)                                                 total.sub.-- length += (*map.sub.-- ptr i!).width + 2*search.sub.--        depth;                                                                         image.sub.-- rects = (BYTE **)malloc(total.sub.-- length*sizeof(BYTE         *));                                                                           if (image.sub.-- rects == NULL)                                                 printf("\n409\n");                                      for(i=0;i<box.sub.-- count;i++){                                                x.sub.-- start = (*map.sub.-- ptr i!).x - search.sub.-- depth;                x.sub.-- end = (*map.sub.-- ptr i!).x + search.sub.- depth                 + (*map.sub.-- ptr i!).width;                                                    x.sub.-- length = (*map.sub.-- ptr i!).width + 2*search.sub.-- depth;         y.sub.-- start = (*map.sub.-- ptr i!).y - search.sub.-- depth;                y.sub.-- length = (*map.sub.-- ptr i!).height + 2*search.sub.--            depth;                                                                           for(j = x.sub.-- start;j < x.sub.-- end; j ++){                                image.sub.-- rects count! = (BYTE *)malloc(sizeof(BYTE)*y.sub.--          length);                                                                           if (image.sub.-- rects count! == NULL)                                         printf("\nSCREWUP 420\n");                              fb.sub.-- rvline(B1,j,y.sub.-- start,y.sub.-- length,image.sub.--         rects count!);                                                                    count++;                                                                     }                                                                           }                                                                             return(image.sub.-- rects);                                                  }                                                                             BYTE **diff.sub.-- box(data.sub.-- box **map.sub.-- ptr, BYTE **fb.sub.--     ptr1, BYTE **fb.sub.-- ptr2,                                                       int box.sub.-- number,int x.sub.-- off, int y.sub.-- off){                unsigned int x.sub.-- length, y.sub.-- length;                                static unsigned int count1=0,count2=0;                                        int i,j;                                                                      static int old.sub.-- number;                                                 BYTE **box;                                                                    x.sub.-- length = (*map.sub.-- ptr box.sub.-- number!).width;                 y.sub.-- length = (*map.sub.-- ptr box.sub.-- number!).height;                if (box.sub.-- number == 0)                                                    old.sub.-- number = 0;                                                       if (old.sub.-- number |= box.sub.-- number){                                   count1 += x.sub.-- length;                                                    count2 += x.sub.-- length + 2*depth;                                          old.sub.-- number = box.sub.-- number;                                       }                                                                             box = (BYTE **)malloc(sizeof(BYTE *)*x.sub.-- length);                        if(box == NULL)                                                                 printf("\nScrewed at line 341 \n");                     for(i=0;i<x.sub.-- length;i++){                                                 box i! = (BYTE *)malloc(sizeof(BYTE)*y.sub.-- length);                        if(box i! == NULL)                                                             printf("\nScrewed at 362\n");                            for(j=0;j<y.sub.-- length;j++)                                                  box i! j! = (BYTE)(abs((int)(fb.sub.-- ptr1 i+count1! j!) -                        (int)(fb.sub.-- ptr2 i+x.sub.-- off+count2! j+y.sub.--                        off!)));                                                             }                                                                             return(box);                                                                }                                                                             BYTE ***diff.sub.-- map(data.sub.-- box **map.sub.-- ptr,int fb1, int         fb2, int search.sub.-- depth){                                                 BYTE ***diff.sub.-- rects,data.sub.-- box, **fb1.sub.-- ptr,                 **fb2.sub.-- ptr;                                                              int count= 0;                                                                 int i,j,k,l;                                                                  unsigned int size, total.sub.-- size = box.sub.-- count*(2*search.sub.--     depth + 1)*                                                                                   (2*search.sub.-- depth + 1);                                   size = 2*search.sub.-- depth + 1;                                             diff.sub.-- rects = (BYTE ***)malloc(sizeof(BYTE **)*total.sub.--            size);                                                                         if (diff.sub.-- rects == NULL)                                                 printf("\nScrewed at 379\n");                            fb1.sub.-- ptr = ram.sub.-- boxdata(map.sub.-- ptr,0,fb1);                    fb2.sub.-- ptr = ram.sub.-- boxdata(map.sub.-- ptr,search.sub.--             depth,fb2);                                                                    for(i=0;i<box.sub.-- count;i++)                                                for(j=0;j<size;j++)                                                             for(k =0;k<size;k++){                                                          diff.sub.-- rects count! = diff.sub.-- box(map.sub.-- ptr,fb1.sub.--     ptr,fb2.sub.-- ptr,i,j,k);                                                         count++;                                                                     }                                                                          return(diff rects);                                                          }                                                                             float *sum.sub.-- rects(data.sub.-- box **map.sub.-- ptr, BYTE                ***diff.sub.-- rects, int search.sub.-- depth,                                       int av.sub.-- flag){                                                   unsigned int i,j,k,l,m,count=0;                                               float size = (2*search.sub.-- depth + 1);                                     float total.sub.-- size = (2*search.sub.-- depth + 1)*(2*search.sub.--        depth + 1)*box.sub.-- count;                                                  float *sum.sub.-- ptr;                                                         sum.sub.-- ptr = (float *)calloc((unsigned int)total.sub.-- size,sizeof(f    loat));                                                                        if (sum.sub.-- ptr == NULL)                                                     printf("\nSCREWUP AT 537\n");                           for(i=0;i<box.sub.-- count;i++)                                                for (j=0;j<size;j++)                                                           for(k=0;k<size;k++){                                                           for(l=0;1 < (*map.sub.-- ptr i!).width; 1++)                                   for(m=0;m < (*map.sub.-- ptr i!).height;m++)                                   sum.sub.-- ptr count! += diff.sub.-- rects count! l! m!;                    if(av.sub.-- flag == ON)                                                       sum.sub.-- ptr count! = sum.sub.-- ptr count!/                                    ((*map.sub.-- ptr i!).width * (*map.sub.-- ptr i!).height);              count++;                                                                     }                                                                           return(sum.sub.-- ptr);                                                      }                                                                             float *var.sub.-- rects(data.sub.-- box **map.sub.-- ptr, BYTE                ***diff.sub.-- rects, int search.sub.-- depth,                                      int av.sub.-- flag){                                                    unsigned int i,j,k,l,m,count=0;                                               float size = (2*search.sub.-- depth + 1);                                     float total.sub.-- size = (2*search.sub.-- depth + 1)*(2*search.sub.--        depth + 1)*box.sub.-- count;                                                  float *var.sub.-- ptr, *av.sub.-- ptr;                                         av.sub.-- ptr = sum.sub.-- rects(map.sub.-- ptr,diff.sub.-- rects,search.    sub.-- depth,av.sub.-- flag);                                                    var.sub.-- ptr = (float *)calloc((unsigned int)total.sub.-- size,sizeof    (float));                                                                        if(var.sub.-- ptr == NULL)                                                     printf("\nTrouble at 477\n");                          for(i=0;i<box.sub.-- count;i++)                                                for(j=0;j<size;j++)                                                             for(k=0;k<size;k++){                                                           for(1=0;1 < (*map.sub.-- ptr i!).width; 1++)                                   for(m=0;m < (*map.sub.-- ptr i!).height;m++)                                   var.sub.-- ptr count! += ((float)(diff.sub.-- rects count! l! m!)      av.sub.-- ptr count!)*((float)(diff.sub.-- rects count! l! m!)                           av.sub.-- ptr count!);                                                  count++;                                                                     }                                                                          free(av.sub.-- ptr);                                                          return(var.sub.-- ptr);                                                      }                                                                             int *min.sub.-- boxset(data.sub.-- box **map.sub.-- ptr, float                *float.sub.-- ptr,int search.sub.-- depth){                                    unsigned int i,j,box.sub.-- jump;                                             float metric,min.sub.-- metric = FLT.sub.-- MAX,position=0;                   int shift 3!;                                                                 div.sub.-- t div.sub.-- result;                                               box.sub.-- jump = (2*search.sub.-- depth + 1)*(2*search.sub.-- depth +       1);                                                                            for(i=0;i<box.sub.-- jump;i++){                                                 metric = 0;                                                                   for(j=0;j<box.sub.-- count;j++)                                                metric += float.sub.-- ptr j*box.sub.-- jump + i!;                           if(metric < min.sub.-- metric){                                                min.sub.-- metric = metric;                                                   position = i;                                                                }                                                                           }                                                                             div.sub.-- result = div((int)position,(int)(2*search.sub.-- depth +          1));                                                                           shift 0! = (int)position;                                                     shift 1! = depth - div.sub.-- result.quot;                                    shift 2! = depth - div.sub.-- result.rem;                                     return(shift);                                                               }                                                                             int main(void)                                                                {                                                                              BYTE ***mock.sub.-- pointer;                                                  char image 12!;                                                               data.sub.-- box **map.sub.-- pointer;                                         int *stats.sub.-- ptr,i;                                                      float *sum.sub.-- ptr3;                                                       int *trans3;                                                                  init.sub.-- itex.sub.-- stuff();                                              fb.sub.-- init();                                                             .sub.-- clearscreen(.sub.-- GCLEARSCREEN);                                    .sub.-- settextposition( 10, 10);                                             select.sub.-- path(B1);                                                       .sub.-- outtext("BASE Image :");                                              scanf("%s", image);                                                           im.sub.-- read(B1,0,0,512,480,image);                                         .sub.-- settextposition( 15, 10);                                             select.sub.-- path(ALOW);                                                     .sub.-- outtext("Image to translate : ");                                     scanf("%s", image);                                                           im.sub.-- read(ALOW,0,0,512,480,image);                                       .sub.-- settextposition(20,20);                                               .sub.-- outtext("Search depth: ");                                            scanf("%d",&depth);                                                           select.sub.-- path(B1);                                                       map.sub.-- pointer = define.sub.-- boxmap();                                  sum.sub.-- ptr3 = var.sub.-- rects(map.sub.-- pointer,mock.sub.--            pointer,depth,ON);                                                             trans3 = min.sub.-- boxset(map.sub.-- pointer,sum.sub.-- ptr3,depth);         printf("\nVARIANCE : pos = %d, x.sub.-- trans = %d, y.sub.--       trans = %d \n",                                                              trans3 0!,trans3 1!,trans3 2!);                                       free(mock.sub.-- pointer);                                                    free(sum.sub.-- ptr3);                                                        free(trans3);                                                                 return 0; }                                                                  __________________________________________________________________________

What is claimed is:
 1. A method for optically imaging blood flow changesin an area of interest, comprising:illuminating the area of interestwith uniform intensity electromagnetic radiation (emr), the emr being inone of the visible and infrared regions of the spectrum; acquiring aseries of control frames representative of the emr absorption of thearea of interest in the absence of a fluorescent dye and processing thecontrol frames to produce an averaged control image; acquiring a seriesof subsequent frames representative of the emr absorption of the area ofinterest in the absence of a fluorescent dye and processing thesubsequent frames to produce an averaged subsequent image; and obtaininga difference image by one of subtracting pixel values in the averagedcontrol image from corresponding pixel values in the subsequent averagedimage, and subtracting pixel values in the subsequent averaged imagefrom corresponding pixel values in the averaged control image toidentify changes in the emr absorption of the area of interest occurringbetween acquisition of the control frames and acquisition of thesubsequent frames, wherein the changes in the emr absorption representchanges in blood flow.
 2. A method according to claim 1, additionallycomprising assigning each pixel having a negative value in thedifference image a value of zero.
 3. A method according to claim 1,wherein the difference image is obtained by subtracting the averagedcontrol image from the subsequent averaged image, and additionallycomprising obtaining a second difference image by subtracting thesubsequent averaged image from the averaged control image and adding thedifference image and the second difference image to obtain a sumdifference image.
 4. A method according to claim 1, wherein thedifference image is obtained by subtracting the subsequent averagecontrol image from the averaged control image, and additionallycomprising obtaining a second difference image by subtracting theaveraged control image from the subsequent averaged image and adding thedifference image and the second difference image to obtain a sumdifference image.
 5. A method according to claim 1, wherein the emr hasa wavelength greater than about 690 nm.
 6. A method according to claim1, additionally comprising mapping positive pixel values in thedifference image to a first color; mapping negative pixel values in thedifference image to a second color; and mapping pixels having a value ofzero to a third color to enhance visibility of blood flow changesindicated by the difference image.
 7. A method according to claim 1,additionally comprising compensating for movement of the area ofinterest by aligning corresponding spatial areas of the averaged controlimage and the averaged subsequent image.
 8. A method according to claim7, wherein the compensating is achieved by means of computations.
 9. Amethod according to claim 1, additionally comprising administering a dyethat migrates to the area of interest prior to acquiring the series ofcontrol frames.
 10. A method for optically imaging blood flowcharacteristics of an area of interest comprising tissue having one ormore blood vessels, the method comprising:illuminating the area ofinterest with uniform intensity electromagnetic radiation (emr), the emrbeing in one of the visible and infrared regions of the spectrum;acquiring a control image representative of the emr absorption of thearea of interest in the absence of a fluorescent dye at one of a controltime point and a control time interval; acquiring a subsequent imagerepresentative of the emr absorption of the area of interest in theabsence of a fluorescent dye at one of a control time point and asubsequent time interval; and comparing the control image with thesubsequent image by one of subtracting the control image from thesubsequent image and subtracting the subsequent image from the controlimage to produce a comparison image that identifies changes in emrabsorption that are indicative of changes in blood flow characteristicswithin one or more blood vessels in the area of interest.
 11. A methodaccording to claim 10, wherein the emr has a wavelength greater thanabout 690 nm.
 12. A method according to claim 10, wherein the controlimage and the subsequent image are obtained as analog video signals andthe analog video signals are amplified and spread across a full dynamicrange.
 13. A method for detecting changes in the oxygenation of blood inan area of interest, comprising:illuminating the area of interest withuniform intensity electromagnetic radiation (emr), the emr being in oneof the visible and infrared regions of the spectrum; detecting aninitial emr absorption level of the area of interest in the absence of afluorescent dye at one of a control time point and a control timeinterval, the initial emr absorption level indicating a baseline levelof blood oxygenation; detecting a subsequent emr absorption level of thearea of interest in the absence of a fluorescent dye at one of asubsequent time point and a subsequent time interval, the subsequent emrabsorption level indicating a subsequent level of blood oxygenation; andcomparing the initial emr absorption level with the subsequent emrabsorption level to detect changes in blood oxygenation over time in thearea of interest.
 14. A method according to claim 13, wherein the emrhas a wavelength greater than about 690 nm.